Adaptive scheduling of electronic messaging based on predictive consumption of the sampling of items via a networked computing platform

ABSTRACT

Various embodiments relate generally to data science and data analysis, computer software and systems, and control systems to provide a platform to facilitate implementation of an interface, and, more specifically, to a computing and data storage platform that implements specialized logic to facilitate predictive consumption of a sample of an item in accordance with an automatically adaptive schedule, for example, via an interface. In some examples, a method may include identifying data representing a replica of an item for transmission to a location associated with an electronic account, identifying a characteristic associated with a user or an item, predicting a date of consumption of the replica, and generating a subset of data included in an electronic message to initiate executable instructions to generate feedback regarding the replica relative to the date of consumption, among other things.

CROSS-REFERENCE TO APPLICATIONS

This application is continuation-in-part (“CIP”) application of U.S.patent application Ser. No. 15/479,230, filed on Apr. 4, 2017, havingAttorney Docket No. ORD-004 and titled “Electronic Messaging toDistribute Items Based on Adaptive Scheduling,” and this applicationclaims priority to U.S. Provisional Patent Application No. 62/425,191,filed on, having Attorney Docket. No. ORD-002P, both of which are hereinincorporated by reference in their entirety for all purposes.

FIELD

Various embodiments relate generally to data science and data analysis,computer software and systems, and control systems to provide a platformto facilitate implementation of an interface, and, more specifically, toa computing and data storage platform that implements specialized logicto facilitate predictive consumption of a sample of an item inaccordance with an automatically adaptive schedule.

BACKGROUND

Advances in computing hardware and software, as well as computingnetworks and network services, have bolstered growth of Internet-basedproduct and service procurement and delivery. For example, onlineshopping, in turn, has fostered the use of “subscription”-based deliverycomputing services with an aim to provide convenience to consumers. Inparticular, a user becomes a subscriber when associated with asubscriber account, which is typically implemented on a remote serverfor a particular seller. In exchange for electronic payment, which istypically performed automatically, a seller ships a specific product (orprovides access to a certain service) at periodic times, such as everythree (3) months, every two (2) weeks, etc., or any other repeatedperiodic time intervals. With conventional online subscription-basedordering, consumers need not plan to reorder to replenish supplies of aproduct.

But conventional approaches to provide subscription-based orderfulfillment, while functional, suffer a number of other drawbacks. Forexample, traditional subscription-based ordering relies on computingarchitectures that predominantly generate digital “shopping cart”interfaces with which to order and reorder products and services.Traditional subscription-based ordering via shopping cart interfacesgenerally rely on a user to manually determine a quantity and a timeperiod between replenishing shipments, after which the quantity isshipped after each time period elapses. Essentially, subscribers receiveproducts and services on “auto-pilot.”

So while the conventional approaches to implementing shopping cartinterfaces may be functional for stable rates of consumption, suchapproaches are not well-suited to facilitate timely reordering ofproducts and services with which consumers may use at rates that varyfrom the fixed periods of time between repeated deliveries. Thus,conventional approaches to reordering or procuring subsequent productand services deliveries are plagued by various degrees of rigidity thatinterject sufficient friction into reordering that cause some users toeither delay or skip making such purchases. Unfortunately, such frictioncauses some users to supplement the periodic deliveries manually if anitem is discovered to be running low more quickly than otherwise mightbe the case (e.g., depleting coffee, toothpaste, detergent, wine, or anyother product more quickly than normal).

Examples of such friction include “mental friction” that may inducestress and frustration in such processes. Typically, a user may berequired to rely on one's own memory to supplement depletion of aproduct and services prior to a next delivery (e.g., remembering to buycoffee before running out) or time of next service. Examples of suchfriction include “physical friction,” such as weighing expending timeand effort to either physically confront a gauntlet of lengthy check-outand shopping cart processes, or to make an unscheduled stop at aphysical store.

Typical online approaches, including conventional shopping cartinterfaces, suffer from less than optimal means with which to reconcilethe different rates of product and service usage of different users. Oneprevalent consequence of mismatches between time periods for deliveringsubscribed products or services and consumption rates by consumers isthat, over time, the supply of a subscribed item is eitherover-delivered or under-delivered. An oversupply of subscribed productor service typically degrades consumer experience due to a number ofreasons. For example, subscribers may believe that a seller is“over-billing” the customer for unneeded products or services.Similarly, an under-supply of subscribed product may give to frustrationand friction that an expected subscribed product or service is scarce orunavailable.

Online retailers and merchants may experience similar consequences dueto mismatching of delivery times and consumption rates, such as at anaggregate level of subscribers. In the aggregate, the mismatches maycause either overstocking or understocking of inventory of the onlineretailers and merchants. Fluctuations in inventory may causenon-beneficial consumption of resources and time. Note, too, that thecomputing systems of online retailers and merchants are not well-adaptedto address the above-described mismatching phenomena when ordering,shipping, and performing inventory management. These types ofsubscription models, therefore, are not generally well-suited forapplication to usual consumption rates of depletable products andservices (e.g., product usage that depletes some or all of the productor service).

In some approaches, online retailers and merchants attempt to increaseexposure and awareness of certain products by, for instance, providingsamples of products to potential consumers. In one approach, a printedpaper, such as a “flyer,” is typically added a box containing apurchased product being readied for shipment (e.g., from a warehouse).The flyer usually conveys information about a product in which aconsumer may purchase in the future. For example, a flyer may include awebsite or other contact information that accompanies a variety ofsnacks and directs a recipient how to order a favored snack from thevariety of snacks. But prior to sampling a product, the “friction”encountered by a consumer to acquire the sample described in a flyer isrelatively high (e.g., a low likelihood of future acquisition of theitem). Consumers have little time to follow directions to receive asample for a product they are not aware that they would like to receive.In a modified approach, a sample of the product (e.g., either free or ata nominal price) may be included in a boxed shipment and the flyer,which explains how to purchase a product should the consumer desire.

Conventional computing platforms that implement traditional onlinemerchant techniques, while functional, suffer drawbacks that limitopportunities to receive information regarding one or more consumers'experiences regarding a product. Known software and applications,therefore, are suboptimal in determining information of consumerexperiences. As manufacturers and retailers expend millions to billionsof dollars in investing in manufacturing samples for distribution, theinformation received back via conventional software and applicationshave yet to reach their potential.

Thus, what is needed is a solution to facilitate techniques ofautomatically predicting an amount of consumption of a sample for anitem according to an adaptive schedule, without the limitations ofconventional techniques.

BRIEF DESCRIPTION OF THE DRAWINGS

Various embodiments or examples (“examples”) of the invention aredisclosed in the following detailed description and the accompanyingdrawings:

FIG. 1 is a diagram depicting an adaptive distribution platform,according to some embodiments;

FIG. 2 is a diagram depicting an example of a conversation platformcontroller, according to some embodiments;

FIG. 3 is a diagram depicting an example of operation of a distributionpredictor, according to some embodiments;

FIG. 4 is a diagram depicting an example of a flow, according to someembodiments;

FIG. 5 depicts an example of another flow, according to someembodiments;

FIG. 6 depicts an example of yet another flow, according to someembodiments;

FIG. 7 is a diagram depicting an example of operation for a distributionpredictor to generate predictive distribution data based on derivedusage data, according to some embodiments; and

FIG. 8 illustrates examples of various computing platforms configured toprovide various functionalities to predict a time of distribution of anitem relative to an adaptive schedule, according to various embodiments;

FIG. 9 is a diagram depicting another example of an adaptivedistribution platform, according to some embodiments;

FIG. 10 is a diagram depicting an example of operation of an informationfeedback predictor, according to some embodiments;

FIG. 11 is a diagram depicting an example of a flow to facilitatepredictive consumption of a sample for an item that implements anautomatically adaptive schedule, according to some embodiments; and

FIG. 12 is a diagram depicting examples of integrated interfaces tofacilitate predictions of sample consumption, according to someembodiments.

DETAILED DESCRIPTION

Various embodiments or examples may be implemented in numerous ways,including as a system, a process, an apparatus, a user interface, or aseries of program instructions on a computer readable medium such as acomputer readable storage medium or a computer network where the programinstructions are sent over optical, electronic, or wirelesscommunication links. In general, operations of disclosed processes maybe performed in an arbitrary order, unless otherwise provided in theclaims.

A detailed description of one or more examples is provided below alongwith accompanying figures. The detailed description is provided inconnection with such examples, but is not limited to any particularexample. The scope is limited only by the claims, and numerousalternatives, modifications, and equivalents thereof. Numerous specificdetails are set forth in the following description in order to provide athorough understanding. These details are provided for the purpose ofexample and the described techniques may be practiced according to theclaims without some or all of these specific details. For clarity,technical material that is known in the technical fields related to theexamples has not been described in detail to avoid unnecessarilyobscuring the description.

FIG. 1 is a diagram depicting an adaptive distribution platform,according to some embodiments. Diagram 100 depicts an example ofadaptive distribution platform 110 that may be configured to facilitateautomatic distribution of items in accordance with an adaptive schedule.For example, adaptive distribution platform 110 may be configured toinitiate electronic messaging (e.g., as reminder messages) of pendingexhaustion of an item, which may be any good or service, to facilitatereplenishment. The timing of the distribution may be adapted to aspecific user 144 (or group of users 144). In the example shown,adaptive distribution platform 110 may include a commerce platformcontroller 112, a distribution predictor 114, and a conversationplatform controller 115. Adaptive distribution platform 110 and any ofits elements, such as commerce platform controller 112, distributionpredictor 114, and conversation platform controller 115, may includelogic, whether implemented in hardware or software, or a combinationthereof.

Commerce platform controller 112 may be configured to perform functionsto support the initiation of the distribution (e.g., shipment) of anitem, among other things. For example, commerce platform controller 112may be configured to facilitate financial-related transactions with oneor more merchant computing systems 130 a, 130 b, and 130 n, including,but not limited to, credit card transactions or the like. In some cases,commerce platform controller 112 may also control participation by usersand their electronic interactions with adaptive distribution platform110. For example, commerce platform controller 112 may manage enrollmentof a user to form an electronic account that enables access via acomputing device, such as one of mobile computing devices 152 a and 152b, to adaptive distribution platform 110.

Distribution predictor 114 may be configured to predict a point in time(or a range of time) at which an item may be exhausted, and based on theprediction, adaptive distribution platform 110 may be further configuredto determine a zone of time (not shown) in which depletion and nearexhaustion of an item is predicted. Further, distribution predictor 114may be configured to determine the zone of time relative to adistribution event. In some cases, a zone of time is determined as arange of time preceding the distribution event. Distribution predictor114 may be configured to associated a point in time with the zone oftime, the point in time defining a moment at which an electronic messagemay be transmitted to a computing device 142, 152 a, or 152 b to informa user of a pending exhaustion of an item and to provide an opportunityto replenish the item in a configurable manner.

Conversation platform controller 115 may be configured to facilitate anexchange of electronic messages and data, as a “conversation,” betweenadaptive distribution platform 110 and any of user computing devices142, which may include mobile computing devices 152 a and 152 b.According to some examples, conversation platform controller 115 may beconfigured to monitor whether to replenish an item, for example, bydetermining whether a particular date coincides with a zone of time. Ifthe particular date coincides with a date range of the zone of time,conversation platform controller 115 may transmit data representing anelectronic message 124 a via one or more networks 120 a and 120 b tomobile computing device 152 b to cause presentation of a remindermessage in an interface 156 b. In response, mobile computing device 152b may transmit data representing an electronic message 124 b to initiatedistribution (e.g., shipment) of an item.

In some examples, conversation platform controller 115 may be configuredto receive an electronic message 122 b originating at user computingdevice 152 a as conversation platform controller 115 monitors whether toreplenish an item. Electronic message 122 b may include datarepresenting an item characteristic (e.g., a product classification,such as “paper towels,” or a product type, such as a brand name (e.g.,“Brand ‘X’”)). In this example, a user 144 may enter a productclassification (“paper towels”) 158 a as displayed in a user interface156 a. Also, conversation platform controller 115 may be configured toinitiate transmission of a control message (e.g., via commerce platformcontroller 112) to one of merchant computing systems 130 a, 130 b, and130 n to initiate distribution of an item for replenishment, whereby amerchant computing system may initiate distribution, shipment, delivery,etc. of the item, by any known means, to a geographic location (e.g., anaddress) associated with an account (e.g., a user account associatedwith user 144). Thus, conversation platform controller 115 may beconfigured to control replenishment of an item regardless of whether anelectronic message that initiates a conversation originates at adaptivedistribution platform 110 or at user computing device 142, 152 a, or 152b.

Adaptive distribution platform 110 may be configured to facilitate“adaptive” scheduling services via a computing system platform formultiple online or Internet-based retailers and service providers, bothof which may be referred to as merchants. In this example, a merchantmay be associated with a corresponding one of merchant computing systems130 a, 130 b, or 130 n that includes one or more computing devices(e.g., processors, servers, etc.), one or more memory storage devices(e.g., databases, data stores, etc.), and one or more applications(e.g., executable instructions for performing adaptive subscriptionservices, etc.). Examples of merchant computing systems 130 a, 130 b, or130 n may be implemented by any other online merchant. Accordingly,adaptive distribution platform 110 can be configured to distribute itemsin accordance with predicted distribution events (e.g., a predicted timeof distribution), any of which may be adaptively derived to optimizedelivery of items.

In view of the foregoing, the structures and/or functionalities depictedin FIG. 1 may illustrate an example of adaptive scheduling toautomatically facilitate the optimal replenishment and distribution ofitems, such as shipping an item that is ordered or reordered inaccordance with various embodiments. According to some embodiments,adaptive distribution platform 110 may be configured to facilitateonline ordering and shipment of a product responsive to an electronicmessage 122 b, 124 b, whereby at least one electronic message 122 b, 124b may be sufficient to complete a transaction with at least one ofmerchant computing systems 130 a, 130 b, and 130 n. Thus, consumption ofresources and time for both users and merchant, as well as associatedcomputing systems, may be reduced such that “friction” of replenishmentis reduced or negated, at least in some cases. In the example shown,computing device 152 a may include an application (e.g., a textmessaging application) configured to receive input via a user interface,such as an input (“paper towels”) 158 a and a destination accountidentifier (“774169”) 154 a via interface 156 a. Computing device 152 bmay also include an application to receive, for example, a user input“NOW” (or any other input) responsive to displayed message 158 b viainterface 156 b. Further to this example, electronic messages 122 b and124 b may be text messages that each includes sufficient information anddata to initiate and complete replenishment of an item as a transaction.In some cases, data representing an identification of a productclassification (or type) and an associated account identifier (e.g., amobile phone number) in text messages 122 b and 124 b may be sufficient.In some examples, adaptive distribution platform 110 may providereplenishment services for multiple entities (e.g., for multiplemerchant computing systems 130), thereby reducing resources thatotherwise may be needed to perform replenishment services individuallyat each merchant computing system 130 a, 130 b, and 130 n.

According to various examples, a distribution event may be predictedautomatically or manually to form an adaptive schedule (e.g., anadaptive shipment schedule). A “distribution event” may refer, at leastin some examples, to an event at which a shipment is to occur (or islikely to occur), or at which an item (e.g., a depletable item) ispredicted to be exhausted. For example, a distribution event (e.g.,either depletion or shipment) may be timed to occur at the 30th dayafter a user has purchased a bottle of vitamin supplements having 30tablets that are taken once a day (e.g., at a depletion rate of 1 tabletper day). In some examples, a distribution event (e.g., a time ofdistribution) may be predicted as a “predicted distribution event”(e.g., a “predicted time of distribution”) based on any number ofsources of information and/or item characteristics, including, but notlimited to, as a usage rate of item by a particular user 144 or apopulation of users 144 associated with one or more merchant computingsystems 130 a, 130 b, and 130 n. In some examples, a usage rate may be afunction of a depletion rate. Further, a “predicted distribution event”may be optimized based on, for example, feedback or results of variousdata analyses. For example, a value representing a predicteddistribution event or date may be optimized by including a user'smonitored preference in predicting a modified distribution event. Aparticular user may prefer to deviate from a predicted distributionevent by delaying or expediting a shipment (e.g., repeatedly pushingback or pulling forward shipment dates). Thus, a reminder message may begenerated in accordance with the user's preference.

According to some examples, a term “order” may be used interchangeablywith “reorder.” In some cases, an “order” may refer to a firsttransaction for a good or service, and a “reorder” may refer tosubsequent transactions. During a first transaction, such as a textrequesting an order to a merchant via adaptive distribution platform110, a prior relationship may not exist with a merchant. If a priorrelationship has yet to exist between user 144 and a merchant computingsystem 130 a, payment information and shipping address information maynot be available to a merchant with which user 144 is engaging. In someexamples, adaptive distribution platform 110 may determine user 144 hasa prior relationship with any other networked merchant computing systems(e.g., systems 130 b and 130 n). Thus, payment information and shippingaddress information may be used from prior orders with merchantcomputing systems 130 b and 130 n. In one case, however, if norelationship exists with either adaptive distribution platform 110 orany merchant computing systems 130 a, 130 b, and 130 n, adaptivedistribution platform 110 may generate an electronic message requestingpayment information and shipping address information. The paymentinformation and shipping address information may be transmitted via anymedium (e.g., website, via phone, email, text messaging, etc.).

In some embodiments, data representing a predicted distribution event orany other data described herein may be implemented by an inventorymanagement controller 131 to manage an amount of inventory to enhanceoptimally the efficacy of fulfilling and replenishing items for anaggregate number of users 144. A merchant computing system, such asmerchant computing system 130 a, may include an inventory managementcontroller 131, which may be implemented as a known inventory managementsoftware application. The inventory management software application maybe adapted to receive an aggregate number of predicted distributionevents for group of users 144 (or an aggregated representation thereof)to refine, for example, amounts of inventory at a storage facility priorto dispatch or replenishment. Adaptive distribution platform 110 may beconfigured to provide a subset of predicted distribution events (e.g.,predicted shipment dates) for an item for respective users.Alternatively, platform 110 may provide an optimal value for a predicteddistribution event for an item to inventory management controller 131.Accordingly, inventory management controller 131 may be configured todetermine an inventory amount (e.g., dynamically) based on the valuesrepresenting one or more predicted distribution events. Therefore, amerchant associated with merchant computing system 130 a may beconfigured to optimally determine an amount or quantity of items forfulfillment in an inventory based on, for example, a predicted shipmentdate and/or a predicted date of exhaustion.

As described, commerce platform controller 112 may facilitatefinancial-related transactions and enrollment of user accounts.According to some examples, commerce platform controller 112 may beconfigured to enroll user 144 and form a corresponding electronicaccount as a data arrangement, which may include data representing anindication that user 144 has interacted at least one time with at leastone of merchant computing systems 130 a, 130 b, and 130 n (i.e., aconsumer has purchased previously a product from a merchant, and a datarelationship between the merchant and the consumer may exist). Based ona previous purchase, commerce platform controller 112 may access data tofacilitate reordering, such as data representing a geographic location(e.g., a shipping address), a payment instrument (e.g., a credit cardnumber, a debit card number, a PayPal™ number, or any other type or formof payment, etc.) and a list of products previously ordered. A usercomputing device identifier, such as a user's mobile phone number, emailaddress, etc., may also be included as data in the electronic account,which may be formed during an enrollment process or upon initiation ofthe distribution of an item (e.g., during reordering). An enrollmentprocess may be implemented as an algorithm that may be executed during acheck-out process so user 144 may opt to include the above-describeddata, including authorization to receive electronic messages (e.g., textmessages), at a mobile computing device. Further, the enrollment processmay be performed at a merchant or a point of sale. For example, acustomer of a health food establishment may be presented with anopportunity to enroll (e.g., provide payment and shipping information).In some cases, the opportunity to avail oneself of the various featuresdescribed herein may be integrated or supplemented with the merchant'sloyalty or member program.

In at least one example, data relating to a user profile acquired in aprevious transaction may be disposed in a merchant repository 174, whichmay include data generated by one or more of merchant computing systems130 a, 130 b, and 130 n. User profile and account data may be stored inuser repository 170, which may include data relating to one or moreusers 144, or stored in a platform repository 172, which may includedata relating to any aspect of data transactions among users 144 andmerchant computing systems 130 a, 130 b, and 130 n used to facilitateoperability of adaptive distribution platform 110.

Commerce platform controller 112 may be configured to facilitatefinancial-related transactions and need not perform a financialtransaction. For example, commerce platform controller 112 may receive acontrol message to initiate distribution of an item. In response,commerce platform controller 112 may transmit transaction-related data(e.g., credit card information, destination address, etc.) to one ofmerchant computing systems 130 a, 130 b, and 130 n, which, in turnapplies payment (e.g., receives credit card authorization) anddistributes an item. Hence, commerce platform controller 112 may providedata transfer of information so that each of merchant computing systems130 a, 130 b, and 130 n may be a merchant of record.

Distribution predictor 114 may include a distribution calculator 116, adistribution optimizer 118, and a zone generator 119. Distributioncalculator 116 may be configured to calculate one or more predicteddistribution events or replenishment-related data to form an adaptiveschedule (e.g., an adaptive shipping schedule). Distribution optimizer118 may be configured to optimize values of predicted distributionevents to, for example, adapt scheduling of distributed items (i.e.,product shipments) to conform (or substantially conform) to delivery orusage preferences of user 144 or a group of users 144. For example,distribution optimizer 118 may be configured to analyze datarepresenting purchasing patterns related to a particular item for aspecific user 144. Based on the results of such an analysis,distribution optimizer 118 may be configured to emphasize certain itemcharacteristics (or values thereof) that may align more closely to auser's ordering or reordering patterns. For example, replenishment of anexact brand name at a later date may be preferred by user 144 oversubstitution of a comparable other brand at an earlier date. Zonegenerator 119 may be configured to define a zone of time, which may beconfigurable or adjusted based on, for example, one or more of userpreferences, an amount of time since a prior distribution (e.g., a priorpurchase), one or more usage rates, units of depletion or depletionrate, etc. An example of a depletion rate is the rate at which 2 unitsof a product are depleted per unit time. To illustrate another example,consider that a bottle of vitamins has 180 tablets and is reordered ordepleted every 72 days. Thus, a predicted rate of depletion may be 2.5per day (e.g., 180 units/72 days). For example, a user may consume 2 to3 tablets per every other day).

Distribution calculator 116 may be configured to receive datarepresenting item characteristics data 102, according to someembodiments, and may be configured further to determine (e.g., identify,calculate, derive, etc.) one or more distribution events based on one ormore item characteristics 102, or combinations thereof (e.g., based onderived item characteristics). For example, distribution calculator 116may compute a projected date of depletion for a particular product, suchas a vitamin product, based on usage patterns and/or ordering patternsof a specific user 144. Note, however, a projected date of depletion mayalso be based on usage patterns and/or ordering patterns of other users144 over any number of merchant computing systems 130 a, 130 b, and 130n. In some examples, a projected data of depletion may be correlated to,or used interchangeably with, the terms a “predicted distributionevent,” a “predicted shipment date,” a “predicted time of distribution,”or the like.

In at least one example, distribution calculator 116 may be configuredto operate on data representing an item characteristic 102, which may bederived or calculated based on one or more other item characteristics102. Examples of item characteristics data 102 may include, but are notlimited to, data representing one or more characteristics describing aproduct, such as a product classification (e.g., generic product name,such as paper towels), a product type (e.g., a brand name, whetherderived from text or a code, such as a SKU, UPC, etc.), a product costper unit, item data representing a Universal Product Code (“UPC”), itemdata representing a stock keeping unit (“SKU”), etc., for the same orsimilar items, or complementary and different items. Itemcharacteristics 102 may also include product descriptions associatedwith either a SKU or UPC. Based on a UPC for paper towels, for example,item characteristics 102 may include a UPC code number, a manufacturename, a product super-category (e.g., paper towels listed undersuper-category “Home & Outdoor”), product description (e.g., “papertowels,” “two-ply,” “large size,” etc.), a unit amount (e.g., 12 rolls),etc. Item characteristics 102 also may include any other productcharacteristic, and may also apply to a service, as well as a servicetype or any other service characteristic. In some examples, datarepresenting item characteristics 102 may be accessed from and/or storedin any of repositories 170, 172, and 174.

A predicted distribution event for an item may be based on a usage rateof the item (e.g., a calculated usage rate), whereby a usage rate may bea rate at which a product or service is distributed (e.g., ordered orreordered), consumed, or depleted. In one example, predicteddistribution of an item for a user 144 may be based on a predicted timeof exhaustion, such as exemplified in the above example in which adistribution event for a bottle of 30 vitamin tablets is predicted tooccur at the 30th day (e.g., relative to a previous delivery). Inanother example, predicted distribution of an item for user 144 may bebased on the user's pattern of purchasing, using, ordering, orreordering the item (or generically similar or complementary items). Forexample, a predicted time of distribution to replenish an item, such asa bottle of ketchup, may be based on a user's past rates ofreplenishment (e.g., shipment rates), such as a median or average timebetween successive requests to distribute reordered items. Adistribution event may be predicted or supplemented by predicting a timeof distribution for ketchup based on rates of past replenishment ofmustard, a complementary product having a usage rates that may correlateto that of ketchup as both items may be used together (and thus consumedat similar depletion rates). Other users' patterns of purchasing, using,ordering, or reordering of the same item (e.g., same brand of vitamin Aat the same merchant) or equivalent item (e.g., different brands ofvitamin A at the same or different merchants) may also be used topredict a time of distribution. For example, consider that user 144 isreplenishing an item, such a vitamin A tablets, at a merchant X.However, there may be negligible information to predict a usage rate (ora time of distribution) for that item at merchant X. Therefore, otherusers' patterns of reordering vitamin A at another merchant, merchant Y,may be used to form a predicted time of distribution for use inpurchasing vitamin A tablets at merchant X.

In some examples, a usage rate to determine a predicted time ofdistribution may be based on identifying distribution rates of an itemrelative to one or more other accounts associated with one or more otherusers or other user computing systems to form an aggregate usage rate.An aggregated usage rate for an item may express, for example, a nominalusage rate that may be used (at least initially) to ascertain predictedtime of reorder with a relatively high degree of confidence. Thus, theaggregated usage rate may be used to generate a predicted time ofdistribution. According to one implementation, usage rates associatedwith other users may originate from item characteristic data accessedfrom one or more merchant computing systems 130 a, 130 b, and 130 n. Theusage rates received from merchants may be to determine a predicted timeof distribution for an item. As an example, usage rates may be derivedby analyzing shipment rates of an item to identify time periods betweendeliveries (i.e., time intervals between order and reorder) relative toone or more merchant computing systems 130 a, 130 b, and 130 n.Thereafter, a usage rate may be used to calculate a predicted time ofdistribution.

Distribution optimizer 118 may be configured to optimize values ofpredicted times of distribution, for example, by adapting scheduleddistribution of items to conform (or substantially conform) to, forexample, usage preferences of user 144 or a group of users 144.Distribution optimizer 118 may configured to receive data representingdistribution-related data from distribution calculator 116, includingpredicted times of distribution, usage rates, etc., as well as itemcharacteristics data 102, to determine one or more optimized times ofdistribution, according to some embodiments. In at least one example,distribution optimizer 118 may be configured to determine an optimaldefault predicted times of distribution with which to establish a timeframe to deliver an item. In some examples, distribution optimizer 118may be configured to modify a value of predicted time of distributionrelative to, for example, a prior shipment, or a receipt of a remindermessage, etc. A value of predicted time distribution may be modified,for example, by an adjustment factor that may be derived throughcomputation or determined empirically. To illustrate, consider that user144 receives electronic messages 124 a at computing system 152 b overmultiple periods of time, whereby user 144 responds via electronicmessages 124 b to request a shipment delay of seven (“7”) days ratherthan ordering at time of that message 158 b is initially received. Inthis example, the adjustment factor may have a value of “7” that maymodify a value of predicted time of distribution to form a modifiedvalue of predicted time of distribution (e.g., modified to 37 days aftera last order of 30 vitamin tablets rather than 30 days). According tosome embodiments, distribution calculator 116 and/or distributionoptimizer 118 may be configured to monitor and update the one or morevalues of the item characteristics. As such, distribution optimizer 118may be configured to dynamically determine a modified time ofdistribution with which to associate with a specific item (e.g., forspecific user 144), among other parameters. Further, distributionoptimizer 118 may be configured to derive an adjustment factor value inaccordance with any number of processes or techniques described hereinor that are otherwise known.

Zone generator 119 may be configured to determine a zone of time as afunction of an item and/or a user, among other parameters, according tovarious examples. Zone generator 119 may form a zone of time (not shown)in which depletion or near exhaustion of an item is predicted. A zone oftime may be relative to a distribution event (e.g., a time ofdistribution). In some cases, zone generator 119 may generate a zone oftime as a range of time either preceding a distribution event, orsubsequent thereto, or both. To illustrate, consider an example in whichan item of paper towels may be exhausted in eight (8) days fromgeneration of a reminder electronic message 124 a. A zone of time mayinclude a range of seven (7) days during which user 144 may select todelay delivery of the item by any day from seven days to one (1) dayprior to the day of exhaustion. Optionally, a zone of time may alsoinclude another range of seven (7) days extending after the day ofexhaustion. Thus, a user may delay or postpone replenishment of an itemwith any time during a range of 14 days. Note that the above-describedvalues defining the example of a zone of time are not intended to belimiting, but may be of any value or number of days in accordance withvarious examples.

Conversation platform controller 115 may include a data interface 117,and may be configured to facilitate an exchange of electronic messagesand data via data interface 117 between adaptive distribution platform110 and mobile computing devices 152 a and 152 b to replenish aconsumable item (e.g., a depletable product). Adaptive distributionplatform 110 may be configured to replenish an item responsive to one ofelectronic messages 122 b and 124 b, according to some examples. In atleast one implementation, adaptive distribution platform 110 mayfacilitate a complete transaction, from online ordering to shipment, ofa product responsive to a unitary electronic message, such as electronicmessages 122 b or 124 b. In at least one example, electronic messages122 b or 124 b are text messages configured to include 160 characters orfewer.

Conversation platform controller 115 further may include logic, whetherimplemented in hardware or software, or a combination thereof,configured to control one or more exchanges of data to identify ordetermine one or more of a user account (and associated user orconsumer), one or more item characteristics, and a destination accountidentifier. According to various examples, adaptive distributionplatform 110 may initiate item replenishment. To illustrate, considerthe following example. Conversation platform controller 115, or anyother element of adaptive distribution platform 110, may include logicconfigured to monitor values of data representing predicted distributionevents for user computing systems 142 and corresponding users 144. Thelogic may also monitor values of data representing a zone of timeassociated with a predicted distribution event. For example,conversation platform controller 115 may compare a point of time (e.g.,a date and/or time, such as December 7, 20XX at 5:00 pm) against a zoneof time (e.g., a date range from December 7, 20XX to December 14, 20XX)associated with a predicted time of distribution for an item, such as“paper towels.” If conversation platform controller 115 detects that apoint of time, such as 5:00 pm on December 7, 20XX, coincides with azone of time for an item “paper towels,” conversation platformcontroller 115 may initiate transmission of an electronic message 124 avia data interface 117 to, for example, user computing device 152 b.Further to the example, December 14, 20XX may coincide with a predicteddate of exhaustion, whereby message 124 a is transmitted at a point oftime that is 7 days prior. Thus, a user has an opportunity toconveniently replenish an item prior to exhaustion.

Data interface 117 may be configured to adapt data transmission to aparticular communication medium and application, as well as dataprotocol, to form electronic message 124 a. For example, data interface117 may adapt electronic messages to implement short message service(“SMS”), as a text messaging service for reception by a mobile computingdevice, including mobile phones, or any other networked computingdevice. An example of SMS is described in one or more standards,including RFC 5724 maintained by the Internet Engineering Task Force(“IETF”), among others. According to some embodiments, electronicmessages 124 a need not be initiated as a function of predicted times ofdistribution. In some cases, electronic messages 124 a (and 122 a) maybe manually-generated (e.g., other than algorithmically triggered).

Electronic message 124 a transmitted to computing device 152 b mayinclude data configured to present via user interface 156 b thefollowing reminder message 158 b to user 144, who is associated with apending exhaustion of paper towels: “You're likely running low on {PAPERTOWELS}. Reply ‘NOW’, if you′d like to order 1 unit of 12 rolls of{BRAND X} paper towels for $17.95 incl. tax and shipping. If you′d liketo place a future order, reply with the number of days when you′d liketo place your order (e.g., text ‘7’ if you′d like your order to beshipped in 7 days from today).” Data representing a destination accountidentifier 154 b may also be transmitted in electronic message 124 a,and, as such, user interface 156 b need only receive at least one userinput by user 144 to effect replenishment. In this example, destinationaccount identifier (“324178”) 154 b may be implemented as a “shortcode”(e.g., five- or six-digit SMS-based shortcode) associated with eitheradaptive distribution platform 110 or one or more of merchant computingsystems 130 a, 130 b, and 130 n. Destination account identifier 154 bmay also be implemented as a phone number or as any other type ofidentifier.

If user 144 desires to ensure a supply of paper towels is not exhaustedat or around the projected date of exhaustion, interface 156 b may beconfigured to receive an input “NOW,” which, in turn, may be transmittedas electronic message 124 b to adaptive distribution platform 110.Conversation platform controller 115 generates a control message 121 to,for example, merchant computing system 130 a to initiate completion ofthe transaction. Optionally, conversation platform controller 115 maygenerate a confirmation electronic message for transmission to usercomputing device 152 b to confirm acceptance of order. Control message121 may include financial data associated with user 144 (e.g., creditcard information) to initiate authorization at merchant computing system130 a, which, in turn, ships the item and optionally sends shippingconfirmation to user computing device 152 b to notify user 144 of anitem in transit.

If user 144 desires to modify a distribution event (e.g., a shipment),interface 156 b may be configured to receive an input “#,” which mayrepresent a value with which to delay the distribution event relative toa predicted distribution event. For example, a user input “7” may bereceived into user interface 156 b. Computing device 152 b then maytransmit data indicated an amount of delay as via electronic message 124b. According to some examples, a value 159 may be presented that isadapted to particular user 144 or item. For example, if user 144historically postpones delivery by 7 days for this or other items, avalue of “7” may be presented. Alternatively, a value 159 may be basedon other users' patterns of postponement. Conversation platformcontroller 115 generates control message 121 for transmission to, forexample, merchant computing system 130 a, which, in turn, initiatesauthorization of the transaction, but with a hold on shipment (until anamount of delay expires). For example, control message 121 may includefinancial data to enable authorization at merchant computing system 130a. Optionally, conversation platform controller 115 may generate aconfirmation electronic message for transmission to user computingdevice 152 b to confirm a “future” order. Subsequently, conversationplatform controller 115 may detect that an amount of delay has elapsed(e.g., 7 days), and, in response, may generate control message 121 tocause merchant computing system 130 a to generate an order, charge thecredit card, ship the item, and optionally send shipping confirmation.

In other examples, a user computing device 152 a may initiate itemreplenishment. For example, user interface 156 a may receive a userinput 158 a of “paper towels,” and, optionally, a destination accountidentifier (“774169”) 154 a, such as a shortcode (or a phone number, anemail address, a URL, etc.). User computing device 152 a may beconfigured to transmit data representing user input 158 a as electronicmessage 122 b, which may also include data representing an accountidentifier (e.g., a mobile phone number) associated with user 144.Conversation platform controller 115 includes logic configured toextract data representing a portion of text, “paper towels,” andanalyzes the extracted data to determine that the text enteredcorrelates to a specific item requested for replenishment. Conversationplatform controller 115, for example, can compare text “paper towels”(or alternatively texted as “papr towls,” or other like short-hand orerroneous entries) to data stored in one or more repositories 170, 172,and 174 to confirm that user 144 is requesting paper towels.

Once the item is identified, conversation platform controller 115 may beconfigured to match “paper towels” against data representing UPCinformation, SKU information, and other information, as well as anyother associations to user 144 based on data linked to the user'saccount identifier. A mobile phone number can be used to link oridentify item characteristics associated with past purchases of papertowels by user 144. In situations in which user 144 has historicallypurchased paper towels with different item characteristics (e.g.,different SKUs, UPCs, etc.), conversation platform controller 115 mayselect an item based on the latest purchase. According to at least oneexample, the above-described actions may be sufficient to complete atransaction with at least one of merchant computing systems 130 a, 130b, and 130 n.

Item replenishment may proceed as follows, according to some furtherexamples. Conversation platform controller 115 may transmit a summaryelectronic message 122 a (not shown) of the item to be replenished, suchas “If you would like to order 1 unit of 12 rolls of {BRAND X} papertowels for $17.95 including tax and shipping, please reply ‘YES.’” Inresponse, user interface 156 a may receive a user input “YES,” therebycausing user computing device 152 a to transmit order affirmation aselectronic message 122 b. Responsive to receiving “YES,” conversationplatform controller 115 may generate a control message 121 fortransmission to, for example, merchant computing system 130 a toinitiate completion of the transaction. Optionally, conversationplatform controller 115 may generate a confirmation electronic message122 a for transmission to user computing device 152 a. An example ofsuch a confirmatory message is as follows: “Your order has successfullybeen placed. Thank you. You'll receive another text once your order hasbeen shipped. In case of questions or problems, please call1-800-555-1234.” Control message 121 may include financial data (e.g.,credit card information) associated with user 144 to initiateauthorization at merchant computing system 130 a, which, in turn, shipsthe item and optionally sends shipping confirmation to user computingdevice 152 a to notify user 144 of an item in transit. Merchantcomputing system 130 a may be configured to generate an order, chargethe credit card, ship the item, and optionally send shippingconfirmation.

User-initiate replenishment may be implemented as follows, at least insome examples. Destination account identifier (“774169”) 154 a may bedisplayed in advertising media (e.g., including printed ads, such as innewspapers and magazines, billboards, and the like), product packing,etc. so a user can review a package of, for example, Brand V proteinpowder to identify shortcode “774169” (or phone number) and the text tobe entered (entry “V powder”) to initiate a replenishment of Brand Vprotein powder (based on linking data related to the mobile phone numberof user 144, such as shipping address, billing information, etc.).Alternatively, destination account identifier 154 a may encode theproduct to replenish. For example, a package or other media (e.g.,printed or online advertisements) may include the following: “ReorderBrand V protein powder by texting ‘NOW’ to 7741,” whereby shortcode 7741may be reserved to order Brand V protein powder at a merchant computingsystem 130. Other examples include According to various other examples,short codes, phone numbers (to text replenishment requests), etc. mayencode any type of information to, for example, uniquely identify one ormore of the following: a specific product, a specific merchant, and aspecifically-configured shipment date and time, among other things.

FIG. 2 is a diagram depicting an example of a conversation platformcontroller, according to some embodiments. Diagram 200 depicts aconversation platform controller 215 including a data interface 217,which, in turn, may include any number of specific interfaces 217 a to217 n, and a transaction controller 280. Each of specific interfaces 217a to 217 n may be configured to facilitate differently formattedexchanges of data via network 220 a (e.g., the Internet) amongconversation platform controller 215, other components of an adaptivedistribution platform (not shown), and a computing device. In thisexample, conversation platform controller 215 may be configured toautomatically facilitate optimal replenishment and distribution ofitems, which may include goods or services, based on an adaptiveschedule using text messages or any other communication medium. Thus,electronic messages may be text messages, electronic mail messages,audio messages, web page messages, or any other messaging technique thatprovide sufficient information and data in a message to initiate andcomplete replenishment of an item. According to some examples, elementsdepicted in diagram 200 of FIG. 2 may include structures and/orfunctions as similarly-named or similarly-numbered elements depicted inother drawings.

Specific interfaces 217 a to 217 n may be implemented in hardware orsoftware, or a combination thereof. In some cases, one of specificinterfaces 217 a to 217 n may be implemented as an applicationsprogramming interface (“API”). For example, interface 217 a may beconfigured to exchange data via HTML, HTTPS, or any other communicationservice/protocol between conversation platform controller 215 and webpage 210. As another example, interface 217 b may be configured toexchange data via POP, SMTP, IMAP, or any other communicationservice/protocol between conversation platform controller 215 andelectronic mail message 220. Interface 217 n may be configured toexchange data via SMS, EMS, MMS (“Multimedia Messaging Service”)services, or any other communication service/protocol betweenconversation platform controller 215 and text message application incomputing device 242 b or a proprietary fulfillment application 241disposed, for example, in computing device 242 a. Thus, conversationplatform controller 215 may be agnostic regarding the various forms ofcommunication channels or media for which data interface 217 providesspecialized interfacing.

Various structures and/or methods described herein may be applied to webpages 210, emails 220, multi-media text messages 248 a and 248 b, amongother forms of communicating request to replenish an item. Web page 210includes an order or reorder page as a user interface 212 to order orreorder an item (e.g., paper towels) by selecting in interface portion214 either immediate delivery via input 211 or delayed delivery viainput 213 and input 219 (a pull-down menu to select an amount of delay).Data received into interface 212 via inputs 211, 213, and 219 may bestored until input (“send”) 218 is activated, after which the data maybe transmitted contemporaneously (or substantially contemporaneously) toconversation platform controller 215 to initiate an order.

An electronic message to remind a user of imminent exhaustion of an itemmay be communicated as an email 220, which is shown to include an itemcharacteristic 222 in a subject line so a user readily may discern theaction required for such a communication. In email body 224, variousdelivery options may be embedded as hypertext links to enable a user toorder or reorder an “Product P” “now,” by selecting link 275 a, orpostponing shipment by 7 days with the selection of link 275 b.According to some embodiments, the presentation of a link for a “7 day”delay, as a first link, may be due to its relatively high degree ofcompatibility with user 144 (based on probabilistic determinations at adistribution predictor, which is not shown). The presentation of a link275 c for a “3 day” delay, as a yet another link, may be due to itssecond-highest degree of compatibility. Thus, an adaptive distributionplatform including conversation platform controller 215 may adaptpresentation of user inputs to accommodate user purchasing andscheduling patterns and preferences to enhance, among other things,users' experiences.

User computing device 242 a may include any messaging application 243configured to transmit electronic messages based on, for example, SMS,MMS, or any other type of messaging service application. For example,FACEBOOK™ Messenger, TWITTER™, TWILIO™, WHATSAPP™, or any other likeapplication may be suitable for implementation as messaging application243, or in support of fulfillment application 241. In some examples,user computing device 242 a may include executable instructionsconstituting a fulfillment application 241, which may be integratedwith, or disposed on (e.g., built on), a messaging application layer(including messaging application 243) to provide enhanced functionality.

Further to user computing device 242 a, consider that a user mayinitiate distribution of an item by capturing an image 248 a of productlabel or code (e.g., UPC), such as by using a camera of user computingdevice 242 a, to import into interface 246 a. User computer device 242 amay transmit image 248 a as an electronic message to an adaptivedistribution platform using shortcode (“774169”) 244 a, whereby theadaptive distribution platform may implement image processing, such asoptical character recognition or other similar processes to identify anitem to replenish based on an image. In some cases, transmitting image248 a from a known mobile phone number may be sufficient to complete thetransaction to ensure that a supply of an item is replenished.

An adaptive distribution platform may initiate distribution of an itemby transmitting message 248 b for presentation in user interface 246 bof user computing device 242 b. As shown, multiple user inputs 249 a and249 b are presented to receive different user inputs to activate atransaction. Selection of link 249 a generates an electronic messagerequesting an immediate order for transmission to a destination accountidentifier 244 b, whereas selection of link 249 b generates anelectronic message that requests an order at a delayed time, as definedby the link (e.g., 7 days). According to various embodiments, certainamounts of delay may be presented in descending order from highestprobability or likelihood (e.g., 7 days) to a lowest probability (e.g.,5 days), whereby the probabilities may be determined based on a user'spreferences and purchasing patterns. Thus, a user likely can find itstop three most probable preferences for the amounts of postponement(e.g., 7, 4, and 5 days) with less expended time than otherwise might bethe case.

Transaction controller 280 may include logic configured to control theexchange of data to identify items for replenishment and other actionsassociated with an order. In text-based requests, transaction controller280 may operate to parse text submitted as an order to determine arequested product. According to some examples, transaction controller280 may operate in accordance with a predictive model (e.g., decisiontree) that operates on XML formatted messages to generate controlmessages to replenish an item. Transaction controller 280 may implementdeep learning, machine learning, neural networks, fuzzy logic,regression techniques, or other computer-based artificial intelligencetechniques to identify a user's request from various types of electronicmessages. Note that the above-described implementation for FIG. 2, aswell as other figures, may be applicable to services in accordance withsome examples. For example, a user may, as an SMS text messagingservice, receive a curated or personalized recommendation as to, forexample, a particular shirt or clothing. Personalization may be based onuser characteristics and preferences, including item characteristics ofpast purchases (e.g., shirt motifs, such as Hawaiian shirts, colors,sizes, etc.). A user need only reply “Yes” to order apparel from thecurated SMS service. As another example, a user may receive an SMS textmessage asking whether the user is interested in delivery of a food itemfor lunch by responding to a text to a food delivery service, a foodtruck, or any other source.

FIG. 3 is a diagram depicting an example of operation of a distributionpredictor, according to some embodiments. As shown, diagram 300 includesa distribution predictor 314 configured to predict a point in time (or arange of time) at which an item may be exhausted or otherwise ought tobe distributed to replenish a supply of an item, which may be any goodor service. A good may be a durable good (e.g., goods that do not wearout quickly or are not depletable per use, such as vehicles, jewelry,appliances, etc.) or disposable goods (e.g., goods that may be used upafter purchase, such as food, toiletries, clothes, and the like). Aprediction may be determined periodically, aperiodically, in real-time,substantially in real-time, at any time, etc. Distribution predictor 314may include a distribution calculator 316, a distribution optimizer 318,and a zone generator 319. According to some examples, elements depictedin diagram 300 of FIG. 3 may include structures and/or functions assimilarly-named or similarly-numbered elements depicted in otherdrawings.

In the example shown, distribution calculator 316 is shown configured togenerate predicted times of distribution subsequent to, for example, aninitial order and distribution (i.e., shipment) at time (“D1”) 363 a.Examples of predicted times of distribution, as shown shipment rateimplementation 360, may include a predicted time (“D₂”) 363 b ofdistribution, a predicted time (“D₃”) 363 c of distribution, predictedtime (“D₄”) 363 d of distribution. Predicted times 363 b, 363 c, and 363d of distribution are predicted distribution events that may be afunction of a user 331 (e.g., a user's usage rates and consumptionpatterns) or an item 333 and its characteristics (e.g., based on usagerates and consumption patterns of a group of users over one or moremerchant computer systems). Note that predicted times 363 b, 363 c, and363 d of distribution need not be predicted as if under a subscription.That is, the depiction of predicted times 363 b, 363 c, and 363 d ofdistribution is for purposes of discussion, and a predicted time ofdistribution need not be predicted beyond a next shipment thatreplenishes an item nearing exhaustion.

Predicted times 363 b, 363 c, and 363 d of distribution may becorrelated to a period of time, such as periods of time p1 and p2, baseda rate of depletion of an item. For example, a period of time, p, maydefine an amount of time (e.g., a time interval) between an initialsupply level (e.g., a full supply on date of initial purchase, such astime (“D1”) 363 a) and a predicted date of exhaustion, such as time(“D₂”) 363 b, which may coincide with a predicted time of distribution(e.g., time of delivery). To illustrate, a distribution event at time(“D₂”) 363 b may be timed to occur at the 30^(th) day after a user haspurchased at time (“D1”) 363 a a bottle of vitamin supplements having 30tablets that are taken once a day. Thus, distribution predictor 314 maygenerate data 322 representing periods 323 of time, such as periods oftime (“p₁”) 362 a and 362 b, both of which are depicted as 1.5 units oftime, and period of time (“p₂”) 362 c is depicted as 1 unit of time. Insome examples, distribution optimizer 318 may receive feedback relatingto ordering patterns of user 331 (e.g., user 331 typically requestsshipments prior to a date of exhaustion). Based on the feedback,distribution optimizer 318 may be configured to adjust a predicteddistribution event at time 363 d and a period 362 c of time, which isshorter than periods 362 a and 362 b of time.

Zone generator 319 may be configured to determine a zone of time inwhich depletion of an item is predicted. A zone of time, and itsduration, may be adapted relative to a distribution event or a period oftime since, for example, a known supply level of an item (e.g., a fullsupply at a previous purchase). As shown, zone generator 319 may beconfigured to determine a zone 368 a of time associated with time 363 b,a zone 368 b of time associated with time 363 c, and a zone 368 c oftime associated with time 363 d, each of which may be a range of timepreceding a distribution event. In some cases, zone generator 319 may beconfigured to determine a zone 368 d of time that succeeds adistribution event at time 363 d. Zone 368 c of time may be different(e.g., shorter) than zones 368 a and 368 b of time based on respectiveperiods of time, according to some examples.

Distribution predictor 314 also may be configured to associate a pointin time with a zone of time, the point in time defining a moment atwhich an electronic message may be transmitted to a computing device(e.g., a mobile phone) to inform a user of the pending exhaustion of anitem and to provide an opportunity to replenish the item in aconfigurable manner. As shown, distribution predictor 314 may associatea point 370 a to zone 368 a of time, a point 370 b of time to a zone 368b of time, and a point 370 c to zone 368 c of time, whereby an adaptivedistribution platform (not shown) may generate electronic remindermessages at points 370 a, 370 b, and 370 c of time. Thus, an electronicreminder message transmitted at, for example, point 370 a of time isassociated with a zone 368 a of time. Consider an example in which anelectronic message is sent at point 370 a of time, which may be eight(8) days prior to time 363 b. Thus, zone 368 a of time may be subdividedinto, or otherwise include, various times at which to delay initiationof an order. For example, an electronic reminder message may be sent toa user eight (8) days prior to time 363 b, whereby the electronicreminder message may provide user inputs to delay distribution by “7,”“6,” “4,” “2,” days, or any other amount of delay, within zone 368 a oftime.

According to some embodiments, distribution predictor 314 may beconfigured to receive and/or determine data for one or more itemcharacteristics that may include, but are not limited to, datarepresenting one or more characteristics of an item, shipmentrate-related data 302, indicator-related data 304, and usage-relateddata 308, and the like. Note that data 302, 304, and 308 may be referredto as examples of item characteristics, according to various examples.Examples of some item characteristics may include a product or producttype, a service or service type, SKU data, UPC data, etc. for the sameor similar items, or complementary and different items (e.g.,complementary or correlatable products may be predicted to have similarpredicted times of distribution or rates of consumption and/ordepletion). Examples of shipment rate-related data 302 may include anumber of purchases or orders per user, per group of users, or per item,a number of shipments, etc. Examples of indicator-related data 304 mayinclude data representing items characteristics that may be correlatableto, for example, order and shipment rate-related data (e.g., a “takerate,” a “cancellation rate,” etc., or any other data type, such as an“adoption rate” of platform-initiated or user-initiated reordering, a“conversion rate” of responding to reminder messages as a function ofuser and item, etc.), which, in turn, may be used to derive a predictedtime of distribution. The term “take rate” may include data representinga rate at which users “take” or implement a presented value of delay(e.g., delay by “7” days) as a default amount of delay, according tosome examples. The term “cancellation rate” may include datarepresenting a rate at which users “cancel” an order (e.g., based on,for example, frustrations of over-supplied or under-supplied amounts),according to some examples.

Examples of usage-related data 308 may include data representingattributes specifying contextual-related information associated with anitem, such as, but not limited to, user-related characteristics, such asdemographic information, purchasing-related data (e.g., purchasepatterns), and the like. In some cases, usage-related data may includeattributes 340 describing items historically purchased by a user 341(e.g., patterns of a parent), as well as attributes 344 describing itemshistorically purchased by associated users 345 (e.g., patterns of agrandparent) or a subpopulation or a population of which a user belongs.Also, usage-related data may include sale-related and shipment-relatedattributes 348 provided by merchant computer systems 349. Datarepresenting attributes 340, 344, and 348 may be stored in repositories342, 346, and 350, according to some examples.

FIG. 4 is a diagram depicting an example of a flow, according to someembodiments. At 402 of flow 400, data representing a zone of time inwhich depletion of an item (e.g., a consumable item) may be predicted.In some examples, the zone of time may be associated with a predicteddistribution event (e.g., a predicted time of distribution), which maybe based on a calculated usage rate related to any of a user, a group ofusers, and one or merchant computer systems. At 404, a predicted time ofdistribution may be monitored relative to a time at which to replenish aconsumable item, for example, prior to exhaustion of an item, which maybe any good or service. At 406, a first electronic message may betransmitted via a network to a user computing device. The firstelectronic message may include one or more item characteristicsassociated with a consumable item to be replenished. Further, datarepresenting the first electronic message may be configured to presentat least one item characteristic (e.g., a product type or brand, aproduct classification (e.g., paper towels), a price, an indication todelay an order, etc.) at a display portion of a user interface.According to various examples, the first electronic message may be areminder message if replenishment is platform-initiated, or aconfirmatory message if user-initiated. At 408, a second electronicmessage from the user computing device may be received into, forexample, an adaptive distribution platform. In some examples, the secondelectronic message may be sufficient to initiate an order, with orwithout a delay, including payment and shipment. At 410, a controlmessage may be transmitted to a merchant computing system to initiatedistribution of a replenishing consumable item. For example, thedistribution of an item may be to a geographic location (e.g., address)associated with an account (e.g., a user account having, for example,data representing a shipping address).

FIG. 5 depicts an example of another flow, according to someembodiments. Flow 500 may apply to platform-initiated replenishment,according to various examples. At 502, a point of time coinciding (orsubstantially coinciding) with a zone of time may be determined. Forexample, data representing a point of time may represent a date (e.g.,as defined by year, month, day and time) that may be compared against adate range associated with a zone of time. When the point of time fallswithin the zone of time, replenishment may be initiated by, for example,and an adaptive distribution platform. At 504, an electronic messagereminding a user may be transmitted. The electronic message may includedata representing one or more item characteristics. Further, theelectronic message may include one or more control user inputs to adaptscheduling of distribution of the item. A control user input, or userinput, may include specifying ordering “NOW,” or at any number ofdelayed units of time (e.g., delay by 3 days). At 506, an electronicmessage that includes a request for replenishment may be received into,for example, an adaptive distribution platform.

At 508, a determination is made whether replenishment may be adjusted.If no adjustment is detected, flow 500 continues to 512, otherwise flow500 continues to 510. For example, if no delay in replenishment isdetected at 508, then flow 500 continues to 512, at which a controlsignal may be transmitted (e.g., to a merchant computing system) toinitiate scheduled delivery of an item, which may be any good orservice. For example, a confirmation electronic message may beoptionally generated at 512 for transmission to a user computing deviceto confirm acceptance of order. The control message may includefinancial data (e.g., credit card information) to initiate authorizationof payment at a merchant computing system. If delay in replenishment isdetected at 508, then flow 500 continues to 510, at which transmissionof the control signal may be recalibrated. For example, a control signalmay be generated for transmission to a merchant computing system toinitiate authorization of the transaction with a hold on shipment. Thus,the recalibrated control signal may provide financial data forauthorization of payment, but may postpone or withhold authorization tocomplete the transaction, including shipment. After an amount of delayhas elapsed (e.g., 7 days), as determined at 510, another controlmessage may be released for transmission to cause the merchant computingsystem to generate an order, charge the credit card, ship the item, andoptionally send shipping confirmation.

At 514, a predicted distribution event may be adjusted, which, in turn,may modify a zone of time. For example, a predicted distribution eventmay be optimized based on feedback or analysis of data trends associatedwith users and merchants. A value representing a predicted distributionevent or date may be optimized by including a user's monitored shipmentdelay preference, whereby the optimized predicted time of distributionmay be a modified distribution event. For example, a particular user mayprefer to deviate from a predicted distribution event by delaying orexpediting shipping (e.g., repeatedly). Thus, a reminder message may begenerated in accordance with the user's preference, which may beembodied in presented amounts of delay or in a modified date ofpredicted exhaustion.

According to various examples described herein, the structures and/orprocesses set forth to replenish items may apply to one or more items(e.g., a single item, or multiple items). In some examples, a point oftime at 502 may be determined that substantially coincides with a zoneof time for multiple items, such as a vitamin regimen in which a userconsumes different vitamin supplement tablets (e.g., vitamins A, B, D,E, etc.) at a daily rate. In some examples, a user may set up via a usercomputing device a list of vitamins to be shipped at a recurring date,or predicted dates based on predicted dates of depletion or exhaustion(e.g., a time of distribution). The list may be entered via mobilephone, email, phone, webpage, etc., whereby and adaptive distributionplatform may manage the tracking and delivering of multiple bottles ofvitamins either in accordance with a list, or by calculating predictedtimes of distribution or delivery (e.g., based on computed or projectedusage rates for each of the vitamins). For example, bottles of 30vitamin tablets may be included in a list every month, whereas bottlesof 60 vitamin tablets may be included in the list every other month.

At 504, an electronic message, such as a text message, and may betransmitted from adaptive distribution platform to a user computingdevice, whereby the electronic message may include a list of itemsassociated with a predicted optimal point in time at which one or moreitems in the list are depleted or substantially depleted. For example,the electronic message may indicate: “The following is a list of itemsthat you may wish to replenish. Reply ‘Yes’ if you wish to replenisheach item in the list, or reply with a corresponding number for thoseitems you wish to replenish. For vitamin A enter ‘1,’ for vitamin Benter ‘2,’ for vitamin C enter ‘3,’ for vitamin E enter ‘4,’ and codliver oil tablets enter ‘5’(separated by space or comma).” A userdesiring to replenish vitamins C and E then would enter and text “3 4”in reply at 506. At 508, one or more items (e.g., bottles of vitamins)may be delayed by any amount of time. For example, a user may enter“1p7d,” “2p1m,” and “5ply,” where “p” indicates an instruction to“postpone” delivery by an amount of time expressed by “d” (day), “m”(month), or “y” (year). In this example, the user is requesting topostpone delivery of vitamin A by 7 days (“1p7d, where 1=vitamin A,p=postpone, 7=number of time units to delay, and d=time units), postponedelivery of vitamin B for one month, and postpone delivery of cod liveroil tablets by one year. The delivery of vitamins A and B, as well ascod liver oil, may be recalibrated at 510 to comply with the user'srequested delivery postponements. At 512, a control signal to shipvitamins C and E may be transmitted to, for example, a merchantcomputing system.

FIG. 6 depicts an example of yet another flow, according to someembodiments. Flow 600 may apply to user-initiated replenishment,according to various examples. At 602, multiple requests forreplenishment for various items may be monitored by, for example, anadaptive distribution platform. Such requests may be associated withvarious zones of time associated with various items. At 604, a requestassociated with a user computing system account for a particular itemmay be detected. In some examples, data encapsulated in an electronicrequest message at 604 may be sufficient to complete an order for itemreplenishment. At 606, data from electronic messages including a requestfor item replenishment may specify, for example, data representing anitem characteristic (e.g., a classification, such as “paper towels,” orproduct type, such as a brand name). For example, a request forreplenishment may include the text “paper towels.” Thus, the text “papertowels” may be extracted for analysis to determine or confirm that thetext entered correlates to a specific item requested for stored itemrepresentations associated with a user computing system account. Forexample, an account identifier, such as a mobile phone number (or emailaddress, etc.), may be associated with a user computing device. Amerchant computing system may include data representing mobile phonenumber, which may be accessible by an adaptive distribution platform.Thus, logic in the adaptive distributions platform may be configured tolink the mobile phone number to data representing a consumer profile,including, but not limited to, a default shipping address, paymentinstrument, etc. The mobile phone number may also be used to identifythe user's past order history to identify past items that may berelevant to the item identified in the extracted data. At 610, a controlsignal may be transmitted to a merchant computing system (e.g., via acommerce platform controller) to initiate scheduled delivery.

FIG. 7 is a diagram depicting an example of operation for a distributionpredictor to generate predictive times of distribution based on derivedusage data, according to some embodiments. Diagram 700 depicts adistribution predictor 714 including an item characteristic correlator717, which may be configured to identify one or more itemcharacteristics 744 with which to correlate to determine or derive datathat may be further used to derive or predict distribution events, timesof distribution, zones of time, and associated points of time, any ofwhich may as encapsulated in predictive distribution data 790. In someexamples, item characteristic correlator 717 may be configured togenerate (or characterize) an aggregated item characteristic 729 thatmay specify attributes of a particular aggregated item characteristicaggregated for a number of users 740 (over users 742 a, 742 b, and 742c).

To illustrate, consider that item characteristic correlator 717 isconfigured to identify usage rates 752 a to 752 c (e.g., a rate at whicha product or service is reordered, or consumed or depleted) forcorresponding user accounts 742 a to 742 c (e.g., associated with userphone numbers). In this example, consider that users 742 a to 742 cpurchase a “laundry detergent” having usage rates 752 a to 752 c. Usagerates between “0” and “1” (e.g., usage amounts during a spring season),usage rates between “1” and “2” (e.g., usage amounts during a summerseason), and usage rates between “2” and “3” (e.g., usage amounts duringa fall season). It may be that users 742 a to 742 c play football duringthe fall, and consequently use more laundry detergent due to footballpractices and games in inclement weather (e.g., due to muddy fields,etc.) Thus, distribution predictor 714 may be able to discern patterns750 of usage. Further, distribution predictor 714 may aggregate theusage rates to form an aggregated usage rate pattern 730 for a group ofusers 740. Based on aggregated usage rate pattern 730, distributionpredictor 714 may be able to generate or predict an aggregateddistribution event or an aggregated time of distribution 790.

Distribution predictor 714 may also use other types of data with whichto evaluate when calculating a predicted time of distribution. Examplesof such data are shown in diagram 700 and may include activitycharacteristics data 781 (e.g., characteristics indicative ofparticipation in a sport or task), geographic characteristic data 782,demographic characteristic data 783 (e.g., aggregated user data),user-specific characteristic data 784 (e.g., history of purchases by auser, etc.), product characteristics data 785, complementary productcharacteristics data 786, and other item characteristics data 787.

Based on the above, distribution predictor 714 may be configured toidentify a usage rate 720 of a new user 799, and further configured tomatch the new usage rate 720 against aggregated usage rate 729 ofaggregated usage rate pattern 730 to predict, for example, that user 799“plays football,” as well as other characteristics of the user withwhich to derive an optimized predicted time of distribution. Accordingto some examples, distribution predictor 714 may predict futureparticipation in an activity or an increase in usage rate duringinterval 728. Thus, distribution predictor 714 may adapt a predictedtime of distribution so as to prepare a user for increased usage ratesby adjusting the periods of time prior to a modified time ofdistribution to reflect an increased laundry detergent amount or adecreased amount of time between shipments. Note that the exampledescribed in diagram 700 is not intended to be limiting to laundrydetergent, but may be applicable to any characteristic of an item orother items.

FIG. 8 illustrates examples of various computing platforms configured toprovide various functionalities to predict a time of distribution of anitem relative to an adaptive schedule, according to various embodiments.In some examples, computing platform 800 may be used to implementcomputer programs, applications, methods, processes, algorithms, orother software, as well as any hardware implementation thereof, toperform the above-described techniques.

In some cases, computing platform 800 or any portion (e.g., anystructural or functional portion) can be disposed in any device, such asa computing device 890 a, mobile computing device 890 b, and/or aprocessing circuit in association with implementing any of the variousexamples described herein.

Computing platform 800 includes a bus 802 or other communicationmechanism for communicating information, which interconnects subsystemsand devices, such as processor 804, system memory 806 (e.g., RAM, etc.),storage device 808 (e.g., ROM, etc.), an in-memory cache (which may beimplemented in RAM 806 or other portions of computing platform 800), acommunication interface 813 (e.g., an Ethernet or wireless controller, aBluetooth controller, NFC logic, etc.) to facilitate communications viaa port on communication link 821 to communicate, for example, with acomputing device, including mobile computing and/or communicationdevices with processors, including database devices (e.g., storagedevices configured to store any types of data, etc.). Processor 804 canbe implemented as one or more graphics processing units (“GPUs”), as oneor more central processing units (“CPUs”), such as those manufactured byIntel® Corporation, or as one or more virtual processors, as well as anycombination of CPUs and virtual processors. Computing platform 800exchanges data representing inputs and outputs via input-and-outputdevices 801, including, but not limited to, keyboards, mice, audioinputs (e.g., speech-to-text driven devices), user interfaces, displays,monitors, cursors, touch-sensitive displays, LCD or LED displays, andother I/O-related devices.

Note that in some examples, input-and-output devices 801 may beimplemented as, or otherwise substituted with, a user interface in acomputing device associated with a subscriber or user account identifierin accordance with the various examples described herein.

According to some examples, computing platform 800 performs specificoperations by processor 804 executing one or more sequences of one ormore instructions stored in system memory 806, and computing platform800 can be implemented in a client-server arrangement, peer-to-peerarrangement, or as any mobile computing device, including smart phonesand the like. Such instructions or data may be read into system memory806 from another computer readable medium, such as storage device 808.In some examples, hard-wired circuitry may be used in place of or incombination with software instructions for implementation. Instructionsmay be embedded in software or firmware. The term “computer readablemedium” refers to any tangible medium that participates in providinginstructions to processor 804 for execution. Such a medium may take manyforms, including but not limited to, non-volatile media and volatilemedia. Non-volatile media includes, for example, optical or magneticdisks and the like. Volatile media includes dynamic memory, such assystem memory 806.

Known forms of computer readable media includes, for example, floppydisk, flexible disk, hard disk, magnetic tape, any other magneticmedium, CD-ROM, any other optical medium, punch cards, paper tape, anyother physical medium with patterns of holes, RAM, PROM, EPROM,FLASH-EPROM, any other memory chip or cartridge, or any other mediumfrom which a computer can access data. Instructions may further betransmitted or received using a transmission medium. The term“transmission medium” may include any tangible or intangible medium thatis capable of storing, encoding or carrying instructions for executionby the machine, and includes digital or analog communications signals orother intangible medium to facilitate communication of suchinstructions. Transmission media includes coaxial cables, copper wire,and fiber optics, including wires that comprise bus 802 for transmittinga computer data signal.

In some examples, execution of the sequences of instructions may beperformed by computing platform 800. According to some examples,computing platform 800 can be coupled by communication link 821 (e.g., awired network, such as LAN, PSTN, or any wireless network, includingWiFi of various standards and protocols, Bluetooth®, NFC, Zig-Bee, etc.)to any other processor to perform the sequence of instructions incoordination with (or asynchronous to) one another. Computing platform800 may transmit and receive messages, data, and instructions, includingprogram code (e.g., application code) through communication link 821 andcommunication interface 813. Received program code may be executed byprocessor 804 as it is received, and/or stored in memory 806 or othernon-volatile storage for later execution.

In the example shown, system memory 806 can include various modules thatinclude executable instructions to implement functionalities describedherein. System memory 806 may include an operating system (“O/S”) 832,as well as an application 836 and/or logic module(s) 859. One or morelogic modules 859 may each be configured to perform at least onefunction as described herein.

The structures and/or functions of any of the above-described featurescan be implemented in software, hardware, firmware, circuitry, or acombination thereof. Note that the structures and constituent elementsabove, as well as their functionality, may be aggregated with one ormore other structures or elements. Alternatively, the elements and theirfunctionality may be subdivided into constituent sub-elements, if any.As software, the above-described techniques may be implemented usingvarious types of programming or formatting languages, frameworks,syntax, applications, protocols, objects, or techniques. As hardwareand/or firmware, the above-described techniques may be implemented usingvarious types of programming or integrated circuit design languages,including hardware description languages, such as any register transferlanguage (“RTL”) configured to design field-programmable gate arrays(“FPGAs”), application-specific integrated circuits (“ASICs”), or anyother type of integrated circuit. According to some embodiments, theterm “module” can refer, for example, to an algorithm or a portionthereof, and/or logic implemented in either hardware circuitry orsoftware, or a combination thereof. These can be varied and are notlimited to the examples or descriptions provided.

In some embodiments, modules 859 of FIG. 8, or one or more of theircomponents, or any process or device described herein, can be incommunication (e.g., wired or wirelessly) with a mobile device, such asa mobile phone or computing device, or can be disposed therein.

In some cases, a mobile device, or any networked computing device (notshown) in communication with one or more modules 859 or one or more ofits/their components (or any process or device described herein), canprovide at least some of the structures and/or functions of any of thefeatures described herein. As depicted in the above-described figures,the structures and/or functions of any of the above-described featurescan be implemented in software, hardware, firmware, circuitry, or anycombination thereof. Note that the structures and constituent elementsabove, as well as their functionality, may be aggregated or combinedwith one or more other structures or elements. Alternatively, theelements and their functionality may be subdivided into constituentsub-elements, if any. As software, at least some of the above-describedtechniques may be implemented using various types of programming orformatting languages, frameworks, syntax, applications, protocols,objects, or techniques. For example, at least one of the elementsdepicted in any of the figures can represent one or more algorithms. Or,at least one of the elements can represent a portion of logic includinga portion of hardware configured to provide constituent structuresand/or functionalities.

For example, modules 859 of FIG. 8 or one or more of its/theircomponents, or any process or device described herein, can beimplemented in one or more computing devices (i.e., any mobile computingdevice, such as a wearable device, such as a hat or headband, or mobilephone, whether worn or carried) that include one or more processorsconfigured to execute one or more algorithms in memory. Thus, at leastsome of the elements in the above-described figures can represent one ormore algorithms. Or, at least one of the elements can represent aportion of logic including a portion of hardware configured to provideconstituent structures and/or functionalities. These can be varied andare not limited to the examples or descriptions provided.

As hardware and/or firmware, the above-described structures andtechniques can be implemented using various types of programming orintegrated circuit design languages, including hardware descriptionlanguages, such as any register transfer language (“RTL”) configured todesign field-programmable gate arrays (“FPGAs”), application-specificintegrated circuits (“ASICs”), multi-chip modules, or any other type ofintegrated circuit.

For example, modules 859 of FIG. 8, or one or more of its/theircomponents, or any process or device described herein, can beimplemented in one or more computing devices that include one or morecircuits. Thus, at least one of the elements in the above-describedfigures can represent one or more components of hardware. Or, at leastone of the elements can represent a portion of logic including a portionof a circuit configured to provide constituent structures and/orfunctionalities.

According to some embodiments, the term “circuit” can refer, forexample, to any system including a number of components through whichcurrent flows to perform one or more functions, the components includingdiscrete and complex components. Examples of discrete components includetransistors, resistors, capacitors, inductors, diodes, and the like, andexamples of complex components include memory, processors, analogcircuits, digital circuits, and the like, including field-programmablegate arrays (“FPGAs”), application-specific integrated circuits(“ASICs”). Therefore, a circuit can include a system of electroniccomponents and logic components (e.g., logic configured to executeinstructions, such that a group of executable instructions of analgorithm, for example, and, thus, is a component of a circuit).According to some embodiments, the term “module” can refer, for example,to an algorithm or a portion thereof, and/or logic implemented in eitherhardware circuitry or software, or a combination thereof (i.e., a modulecan be implemented as a circuit). In some embodiments, algorithms and/orthe memory in which the algorithms are stored are “components” of acircuit. Thus, the term “circuit” can also refer, for example, to asystem of components, including algorithms. These can be varied and arenot limited to the examples or descriptions provided.

FIG. 9 is a diagram depicting another example of an adaptivedistribution platform, according to some embodiments. Diagram 900depicts an example of adaptive distribution platform 910 that may beconfigured to facilitate automatic distribution of items in accordancewith an adaptive schedule. Adaptive distribution platform 910 may beconfigured to exchange one or more electronic messages via networks 920a and 920 b with one or more of computing devices 942 and 952 b forusers 944, which may be a consumer. In the example shown, adaptivedistribution platform 910 may include a distribution predictor 914 aswell as other elements (not shown), any of which may include logic,whether implemented in hardware or software, or a combination thereof.Distribution predictor 914 is shown to include a distribution calculator916, a distribution optimizer 918, and a zone generator 919. Further,adaptive distribution platform 910 may also be configured to access oneor more of a user repository 970 configured to store at least datadescribing user characteristics, a platform repository 972 configured tostore platform-related characteristics in data, including itemcharacteristic data 902, and a merchant repository 974 configured tostore user-identification data as well as merchant-related information(e.g., production information, inventory information, etc.).User-identification data may be based on, for example, a uniqueidentifier including on one or more of a location (e.g., a consumer'sshipping address), a payment instrument identifier (e.g., a credit cardnumber, etc.) and an electronic account identifier (e.g., identified bya mobile phone number or the like), according to some embodiments.According to some examples, elements depicted in diagram 900 of FIG. 9may include structures and/or functions as similarly-named orsimilarly-numbered elements depicted in other drawings, such as FIGS. 1to 3 and FIG. 8, among others.

Adaptive distribution platform 910, as described herein, may beconfigured to facilitate “adaptive” scheduling services, as describedherein, via a computing system platform for multiple online orInternet-based retailers and service providers, both of which may bereferred to as merchants. In this example, a merchant may be associatedwith a corresponding one of merchant computing systems 930 a, 930 b, or930 n that includes one or more computing devices (e.g., processors,servers, etc.), one or more memory storage devices (e.g., databases,data stores, etc.), and one or more applications (e.g., executableinstructions for performing adaptive subscription services, etc.).Examples of merchant computing systems 930 a, 930 b, or 930 n may beimplemented by any other online merchant.

Distribution predictor 914 is further shown to include an informationfeedback predictor 914 a, which may be configured to predict a date ofconsumption with which to solicit feedback regarding a replica of anitem, the replica being a portion or “sample” of an item. A replica,therefore, may be one or more units of an item configured to provide arecipient an opportunity to experience the item, whereby a recipient maylearn information regarding the item (e.g., learning informationregarding the benefits of using the item, such as the health-relatedbenefits of “Omega-3” fatty acid-based health supplements). Units of anitem may describe a serving size, such as a number of Omega-3 tablets, avolumetric amount (e.g., a number of milliliters or ounces of shampoo),and the like.

According to various examples, information feedback predictor 914 a maybe configured identify one or more samples of items to transmit to alocation associated with an electronic account for consumer 944.Further, information feedback predictor 914 a may be configured toautomatically generate a communication channel 926 to facilitatecommunication via one or more electronic messages 924 a and 924 b toidentify whether a sample, for example, is consumed and the resultstherefrom. In at least one example, electronic message may be formattedand transmitted as a short message service (“SMS”) message orequivalents thereof. In at least some embodiments, information feedbackpredictor 914 a may be configured to generate data automatically at adetermined point in time (or time interval) that may be optimized tosolicit feedback by transmitting an electronic message requestingfeedback of a consumed sample. For example, data representing a requestfor feedback may be transmitted as electronic message 924 a at a pointin time at which a particular consumer 944 may likely have predictivelyconsumed at least one unit of consumption to render a position forgenerating feedback information and data.

Diagram 900 depicts information feedback predictor 914 a as including aconsumption calculator 916 a, a feedback optimizer 918 a, and a feedbackzone generator 919 a, each of which may include logic, whetherimplemented in hardware or software, or a combination thereof, similaror equivalent to that in distribution calculator 916, distributionoptimizer 918, and zone generator 919, respectively. Logic in one ormore of distribution calculator 916, distribution optimizer 918, andzone generator 919 may be modified or supplemented to facilitate thefunctionality of consumption calculator 916 a, feedback optimizer 918 a,and feedback zone generator 919 a, according to at least some examples.In one example, consumption calculator 916 a may be configured tocalculate a time (e.g., a time interval) at which one or more units ofconsumption of a sample may be consumed. Responsive to the calculatedtime, information feedback predictor 914 a may generate an electronicmessage requesting feedback at this point.

In another example, feedback optimizer 918 a may be configured tooptimize a determination of a time at which one or more units ofconsumption of a sample may be consumed. Hence, feedback optimizer 918 amay optimize a calculated time of consumption as determined byconsumption calculator 916 a. In particular, feedback optimizer 918 amay be configured to adapt a computed time interval in which consumptionis likely to predict a date of consumption by user 944 based on, forexample, user characteristics, such as, but not limited to, a consumer'susage rate (or “consumption rate”) of an item, demographiccharacteristics of consumer 944, data representing patterns of historicpurchasing and/or consumption behaviors, and the like. For example,consumer 944 may historically consume vitamins or supplements at a rateless than recommended (e.g., a user may consume 1 tablet a day ratherthan the recommended 2 tablets a day), and, thus, consumer 944 mayconsume an item at a different rate. Hence, feedback optimizer 918 a maybe configured to adapt optimally data representing consumption rates(and dates) to conform to consumption (e.g., usage) patterns of aparticular user 944. Responsive to data representing characteristics ofa user and/or item, information feedback predictor 914 a may generate anelectronic message requesting feedback at this point.

In yet another example, feedback zone generator 919 a may be configuredto adjust an adapted consumption date or range of dates, responsive todata generated at a user computing device, such as computing device 952b. For example, a user computing system 942 and/or associated electronicaccount for consumer 944 may include data indicating whether a unit ofconsumption may have been consumed or may be pending (e.g., a user 944may yet to sample a portion of an item during an interval of time).Thus, an input may be received as data signals representing user inputinto interface 956 b to initiate, for example, an adjusted interval oftime in which to solicit feedback. Consider that user 944 may havereturned from a two (2) week vacation and received a package containinga sample. The two week delay may be accommodated or addressed byenabling mobile computing device 952 b (and/or adaptive distributionplatform 910) and associated software applications to adjust the periodin which to provide feedback.

To illustrate operation of information feedback predictor 914 a,consider an example in which at least one sample 927 a is included in ashipment of a purchased item transmitted via, for example, U.S. PostalService or any other carrier. Information feedback predictor 914 a maybe configured to predict a date of consumption of at least a part of areplica or sample 927 a. Note that sample 927 a can be shipped orobtained independently from a purchased item. According to variousexamples, a predicted date of consumption may be based on one or moreuser characteristics and/or one or more item characteristics, forexample. Examples of user characteristics include data representingattributes specifying contextual-related information associated withuser 944, such as, but not limited to, user-related characteristics,such as demographic information, purchasing-related data (e.g., purchasepatterns), and the like. In some cases, usage-related data may includeattributes describing items historically purchased by a user, as well asan electronic account identifier, a payment instrument identifier (e.g.,a credit card number), a location (e.g., an address), and otheruser-related information. User characteristics may also be mapped orotherwise associated with one or more item characteristics. For example,a usage rate of an item may be associated with a particular user (e.g.,consumer 944 consumes or uses a product, such as shampoo, at aparticular rate of consumption or depletion). Also, user characteristicsmay also include aggregated user characteristics that representuser-related information of a subpopulation from which predictionsregarding, for example, selection of sample 927 a or a consumption datemay be determined. Examples of item characteristics include, but are notlimited to, one or more other item characteristics 102 of FIG. 1.

Referring back to FIG. 9, information feedback predictor 914 a may beconfigured to receive data representing a time of disposition orshipment of sample 927 a, which may be used as a reference with which topredict consumption, and, in turn, predict a time to generate anelectronic message 924 a to solicit feedback. In at least one example,data representing a time of disposition or shipment of sample 927 a maybe generated by one or more of merchant computing systems 930 a, 930 c,and 930 n. For example, data may be generated at any of merchantcomputing systems 930 a, 930 c, and 930 n, or at adaptive distributionplatform 910 or any other computing device, whereby the data may beconfigured to cause shipment of a replica 927 a or sample to a locationassociated with an electronic account. As such, printed material orinformation regarding the samples may be viewed as superfluous, and neednot be included in a shipment (e.g., excluded from a container or boxincluding a sample), according to at least some implementations.

Information feedback predictor 914 a may be configured to generate asubset of data 924 a representing an electronic message (or a portionthereof) that may be transmitted to mobile computing device 952 b tocause or otherwise initiate, for example, execution of instructions togenerate feedback regarding sample 927 a. As shown, subset of data 924 amay cause user interface 956 b to generate a message 958 b with userinputs 959 and 961, among others, to determine a status of sample 927 a(e.g., whether a sample has been received and consumed, which may be abasis from which to render feedback). In the example shown, userinterface 956 b displays the following: “How do you like SAMPLES x, y,z, . . . ? Reply X to order product for $X.XX, or Reply Y to orderproduct Y for $Y.YY, or Reply ALL to order all.” Also, user interface956 b may also display the following: “If additional time is needed toprovide feedback, please respond with “#” (i.e., a number) of days atwhich to check back.” Subset of data 924 a may also include datarepresenting a destination account identifier (“324178”) 954 b, to whicha feedback response may be transmitted as electronic message 924 b.According to some examples, user 944 may initiate generation ofelectronic message 924 b, which includes feedback, via destinationaccount identifier (“324178”) 954 b (or by any mode of communication) toindicate a sample has been consumed and feedback (e.g., including apurchase) may be available.

According to some examples, consumer 944 may cause generation of aninput signal via user input 959 to order item “X” (e.g., based on sample927 a), order item “Y” (e.g., based on sample 927 b), order other items(not shown), or order “All.” Adaptive distributive platform 910 and/ormerchant computing systems 930 a, 930 b, and 930 n may assign productidentifiers (e.g., SKUs) to a sample for treatment by platform 910 asany item, according to a specific implementation. Data representing userinput 959 may be transmitted via electronic message 924 b. Thus, aresponse may be interpreted positively, according to at least someembodiments, when a response confirms an interest in acquiring orpurchasing an item (e.g., in accordance with a subscription or otherdistribution and online sales arrangements as described herein).According to some examples, a lack of response to electronic message 924a may be viewed as negative or a lack of interest related to samples 927a and 927 b.

In at least one example, user 944 may have yet to consume either sample927 a or 927 b, and may further request additional time (e.g.,additional number of days) via user input 961 to provide feedback. Datarepresenting a delayed request may be transmitted as electronic message924 b. Accordingly, information feedback predictor 914 a may includelogic configured to predict an adapted time or timing to providefeedback to enable consumer 944 to sufficiently enjoy or experience asample 927 a or 927 b to provide meaningful feedback. Thus, an input 961(“#”) may be received as data signals representing user input intointerface 956 b to initiate, for example, an adjusted interval of timein which to solicit feedback in situations, for example, when user 944has yet to open a package containing a sample. The delay may beaddressed by enabling mobile computing device 952 b (and/or adaptivedistribution platform 910) and associated software applications toadjust the period in which to provide feedback. User 944 may select aparticular units of time (e.g., days) with which to postpone providingfeedback so as to consume a sample, such as sample 927 a.

According to some examples, information feedback predictor 914 a may beconfigured to analyze user characteristics associated with, for example,user 944 to match or align types and attributes of samples that user 944may be likely to consume and provide feedback. For example, considerthat information feedback predictor 914 a may identify usercharacteristics that indicate user 944 likely is (1) a young parent(e.g., based on historic purchases of baby formula, diapers, babypowder, etc.), is (2) likely a pet owner (e.g., based on past purchasesof dog food, puppy treats, rawhide bones, etc.), and (3) may be avegetarian (e.g., based on previously-stored grocery purchases thatexclude meat, meat-related items, or the like). Based on such exemplaryuser characteristics, information feedback predictor 914 a may beconfigured to select a replica of an item as a sample. For example,information feedback predictor 914 a may select a sample 927 a as asample of “diaper cream,” a sample of a “bacon-flavored dog treat,” or asample of spices, herbs, or vegetables, any of which may be selected toenhance accuracies in predicting an invoked response (e.g., feedback)via electronic message 924 b. By contrast, and further to the aboveexample, information feedback predictor 914 a may be configured todeemphasize “cat-related” items, “geriatric-related” items, or“meat-related” items as sample 927 a for user 944.

In at least some examples, information feedback predictor 914 a may beconfigured to determine one or more other samples 927 b based on, forexample, sample 927 a. For instance, consider sample 927 a may be any ofthe followings items: “catsup,” “shampoo,” or a “first product” of Brand“X.” Information feedback predictor 914 a may be configured to determinesample 927 b as a complementary item that, for example, may have arelatively high correlation of synchronous usage and consumption, and,thereby, a likelihood of sampling by user 944. Thus, sample 927 b may beone of “mustard” and “conditioner,” which are respective compliments of“catsup” and shampoo.” A “second product” of Brand “X” may be selectedas sample 927 b based on, for example, determining a likelihood ordegree of similarity between items or predicting brand loyalty based onpast purchases of Brand X products, whereby user 944 may be likely tosample the first product of Brand X and provide feedback.

In at least one example, information feedback predictor 914 a may beconfigured to analyze data representing user-related characteristicsassociated with the electronic account (e.g., for user 944) againstother user-related characteristics associated with other electronicaccounts (e.g., for users other than user 944). In this example,information feedback predictor 914 a may be configured to identifyattributes of users and items common with user 944 to identify auniverse of items having a relatively high likelihood of being consumedor purchased by a group of users including user 944. For example, agroup of users residing in a northwest portion of the United States(e.g., the state of Maine, which experiences cold, wintry seasons) mayhave previously purchased “snow boots” having sizes associated with“elementary-aged” children. Thus, a common other item, such as“mittens,” may be identified as being previously purchased by the otherusers for weather activities except user 944 (at least in this example).Therefore, information feedback predictor 914 a may be configured todetermine “mittens” as sample 927 b (e.g., either at a discounted costor without charge). “Mittens” may be viewed as a target item forsampling by user 944 based on data associated with the other electronicaccounts.

Information feedback predictor 914 a may be configured to implement anyanalytical determination to correlate and classify users and/or itemsfor predicting samples, according to various examples. According to someembodiments, information feedback predictor 914 a may be configured toclassify and/or quantify various user and item attributes by, forexample, applying machine learning or deep learning techniques, or thelike. In one example, information feedback predictor 914 a may beconfigured to segregate, separate, or distinguish a number of datapoints representing similar (or statistically similar) attributes,thereby forming one or more clusters or groups of data (not shown). Theclustered data may be grouped or clustered about a particular attributeof the data, such as a source of data (e.g., a channel of data), a typeof language, a degree of similarity with synonyms or other words, etc.,or any other attribute, characteristic, parameter or the like. While anynumber of techniques may be implemented, information feedback predictor914 a may apply “k-means clustering,” or any other known clustering dataidentification techniques. In some examples, information feedbackpredictor 914 a may be configured to detect patterns or classificationsamong datasets and other data through the use of Bayesian networks,clustering analysis, as well as other known machine learning techniquesor deep-learning techniques (e.g., including any known artificialintelligence techniques, or any of k-NN algorithms, regression, Bayesianinferences and the like, including classification algorithms, such asNaïve Bayes classifiers, or any other statistical or empiricaltechnique).

In view of the foregoing, the structures and/or functionalities depictedin FIG. 9 may illustrate an example of adaptive scheduling forelectronic messages to automatically facilitate feedback at optimalintervals of time during which a user may likely have consumed a sampleto provide an assessment on whether to, for example, order or subscribefor delivery of an item based on the sample. Should a user desire not toprovide feedback or generates negative feedback (e.g., does not wish topurchase an item), data representing the sample may be stored forimproving accuracy in predicting which samples a user may likely consumeand purchase in the future. According to some examples, optimalintervals of time in which to transmit a request for feedback may bebased on predicted dates of consumption with which to enhance accuracyof soliciting feedback relatively close to consumption so a user mayprovide precise feedback while being “most receptive” to purchasingwhile the experience from the sample is “fresh” in one's mind. Accordingto at least one implementation, an electronic message or application mayinclude executable instructions to enable a user to adjust a date atwhich to provide feedback, which, in turn, enables a user to accommodateor plan consumption of a sample for purposes of rendering accuratefeedback timely.

By enhancing accuracy of relevant information pertaining to a sample,information feedback predictor 914 a may generate additional analyticinformation and insights into whether a particular sample is, forexample, enhancing a “sample-to-purchase” conversion metric thatdescribes, for example, a ratio of a number of items purchased against anumber of samples sent. With improvements in said metric, fewerresources may be consumed or expended unnecessarily without a return oninvestment. According to various examples, adaptive distributiveplatform 910 and/or information feedback predictor 914 a, or any otherelement, may be configured to store data memorializing electronictransactions and messages associated with predicting consumption ofsamples and feedback about the samples. Therefore, stored electronictransaction and electronic message data may be used to further refine apredicted date of consumption, as well as refinements in selecting asample and modifying a feedback response interval of time, according tovarious examples. Logic, including hardware or software, or acombination thereof, may facilitate one or more structures or functionsof adaptive distributive platform 910 and/or information feedbackpredictor 914 a, or any other element to effectuate enhanced predictionof consumption of a sample for generating adaptively-scheduledelectronic messages.

FIG. 10 is a diagram depicting an example of operation of an informationfeedback predictor, according to some embodiments. As shown, diagram1000 includes a distribution predictor 1014 implementing an informationfeedback predictor 1014 a that may be configured to predict a point intime (or a range of time) at which a sample of an item (e.g., any goodor service) may be exhausted or depleted, or a point in time (or a rangeof time) at which a sample is consumed prior to depletion. Distributionpredictor 1014 may include an information feedback predictor 1014 a,which, in turn, may include a consumption calculator 1016 a, a feedbackoptimizer 1018 a, and a feedback zone generator 1019 a. Optionally,information feedback predictor 1014 a may be configured to include anitem characteristic correlator 1017, which may be structurally and/orfunctionally similar to that of FIG. 7, whereby item characteristiccorrelator 1017 may be configured to identify one or more itemcharacteristics with which to correlate to determine or derive data thatmay be further used to derive or predict consumption events (e.g., unitsof consumption), times of distribution or shipping, times of delivery, afeedback commencement time (e.g., a point in time or a range of time inwhich feedback may commence), and the like, similar to described in FIG.7. According to some examples, elements depicted in diagram 1000 of FIG.10 may include structures and/or functions as similarly-named orsimilarly-numbered elements depicted in other drawings.

Diagram 1000 also depicts distribution predictor 1014 and informationfeedback predictor 1014 a being configured receive item characteristicdata 1004 and user characteristic data 1008 from, for example,repositories 1042, 1046, and 1050. In some cases, user-related and/orusage-related data 1008 may include data representing attributes 1040that, for example, describe demographic characteristics (e.g., location,age, gender, familial status, etc.) for items historically purchased bya user 341 (e.g., data representing patterns of a parent, patterns of apet owner, patterns of a particular sports fan, etc.). Data representingattributes 1044 may describe items historically purchased by other users1045 (e.g., patterns of a group of grandparents, patterns of a group ofpet owners, patterns of a group of sports fan for a particular team,etc.). Attributes 1044 may represent an aggregation of a subpopulationor a population of which a user belongs. Also, usage-related data mayinclude sale-related and shipment-related attributes 1048 provided bymerchant computer systems 1049. Data representing attributes 1040, 1044,and 1048 may be stored in repositories 1042, 1046, and 1050, accordingto some examples. Examples of some item characteristics in data 1004 mayinclude a product or product type, a service or service type, SKU data,UPC data, etc. for the same or similar items, or complementary ordifferent items (e.g., different items that may have correlatablesamples of products that can be predicted to have similar predictedrates of consumption and/or depletion, and may be have a likelihood of afeedback response by a recipient).

Information feedback predictor 1014 a may be configured to generateddata 1022 to predict, among other things, a date of consumption withwhich to solicit feedback regarding a replica of an item. A date ofconsumption may be predicted to occur (e.g., statistically) in afeedback commencement interval or time 1090 during which user 1031 maybe predicted to have received a sample 1033 of item 1034. Thus,predicted points in time 1063 c, 1063 d, and 1063 e of consumption(e.g., a predicted time of consumption of a sample) may be a function ofa user 1031 (e.g., a user's usage rates and consumption patterns) and/oran item 1034 and its characteristics (e.g., based on usage rates andconsumption patterns of a group of users over one or more merchantcomputer systems). Sample 1033 may be representative of item 1034 and,hence, may include its characteristics. Note that feedback commencementinterval 1090 may represent a single point in time, or any number ofpoints in time, according to some examples.

Information feedback predictor 1014 a may be configured to receive datafrom a merchant computing system 1049 that a sample 1033 has shipped toa location at a time (“S”) 1063 a. Further, information feedbackpredictor 1014 a may be configured to receive data representing thatsample 1033 has been delivered to a location at time (“R”) 1063 b. Assuch, information feedback predictor 1014 a may identify time 1063 b asa beginning of feedback commencement time 1090. Consumption calculator1016 a may be configured to calculate a time (“C₁”) 1063 c, time (“C₂”)1063 d, or time (“C₃”) 1063 e (or a time interval) at which one or moreunits of consumption of a sample may be consumed. According to someexamples, times 1063 c, 1063 d, and 1063 e, as well as interval 1090,may be based on usage rates or rates of depletion.

Feedback optimizer 1018 a may be configured to adapt a computed timeinterval 1090 in which consumption is likely to predict a date ofconsumption by user 1031 based on, for example, user characteristics.Hence, feedback optimizer 1018 a may be configured to extend or reduce asize of interval 1090 by adjusting one or more points by modifyingbeginning or an ending time (e.g., shifting the interval in directions1081 or 1083). Feedback zone generator 1019 a may be configured toadjust an adapted consumption date or range of dates, responsive to data1024 generated at a user computing device. For example, a user mayrequest to extend a feedback response time to point of time (“F”) 1063f. Thus, feedback zone generator 1019 a may be configured to receiveadjustment data responsive to data 1022 to generate feedback, theadjustment data configured to modify a predicted date of consumption totime 1063 f (or another time). As such, feedback zone generator 1019 acan be configured to cause generation of another subset of data to askor solicit feedback at later date by generating ancillary datarepresentative of another request for a feedback response from user1031. User 1031 may generate feedback data 1024 including, for example,a request to purchase or subscribe to item 1034 at time (“D1”) 1063 f.Feedback other than a purchase, whether positive or negative, may alsobe included in data 1024 (e.g., a request for an additional sample). Attime 1063 f, data representing a feedback response to accept a unit ofthe item in an online purchase may be received, whereby scheduling oftransmission of a unit of the item to the location (associated withelectronic account) may be generated to initiate successive shipments ofthe item to user 1031.

Information feedback predictor 1014 a may be configured to transmit arequest of feedback via electronic message data 1022 at any time infeedback commencement time 1090. For example, information feedbackpredictor 1014 a may transmit electronic message data 1022 to user 1031at time (“C₁”) 1063 c, time (“C₂”) 1063 d, or time (“C₃”) 1063 e, any ofwhich may be selected based on user characteristics and/or itemcharacteristics. For example, user 1031 may be likely to render feedbackafter a first unit of consumption at time 1063 c for “shampoo,” whereasuser 1031 may likely require additional time to render feedback for asample of a “health supplement,” which may take several days toexperience (e.g., two tablets per day for a two weeks). Thus, an itemcharacteristic of “product classification” (e.g., classification of“shampoo” in contrast to “health supplements”) may be used to determineor predict a point in time to transmit a request for feedback duringtime interval 1090. In some cases, user characteristics of user 1031 mayalso be used to select one or time (“C₁”) 1063 c, time (“C₂”) 1063 d, ortime (“C₃”) 1063 e. If user 1031 is determined or predicted to belong toa household of two to four persons (e.g., a family), then additionaltime may be provided to ensure multiple persons may experience sample1033. Thus, a subsequent time 1063 e may be selected rather than a firsttime 1063 c. Other user characteristics and item characteristics may beused to select an optimal time at which to solicit feedback, accordingto various examples.

FIG. 11 is a diagram depicting an example of a flow to facilitatepredictive consumption of a sample for an item that implements anautomatically adaptive schedule, according to some embodiments. At 1102of flow 1100, data representing a replica of an item, as a sample, maybe identified for transmission to a location associated with anelectronic account. For example, a selected sample may be included in acontainer being shipped with purchased goods, or may be shippedseparately, to a residential address associated with an electronicaccount (e.g., an online account with which to purchase items onlinefrom a merchant).

At 1104, a characteristic of a user or an item, or both, may beidentified. For example, an item characteristic may be identified, suchas a product classification (e.g., generic product name, such asshampoo), a product type (e.g., a brand name), etc., any of which may beused to describe a sample. Further, user characteristics (e.g., pastusage or consumption patterns, including past purchasing patterns), andthe like, may be used to predict a date of consumption for providing atimely acquisition of a response from a recipient of a sample.

At 1106, a date of consumption of a replica may be predicted to form apredicted date of consumption as a function of, for example, one or morecharacteristics (e.g., one or more user characteristics and/or one ormore item characteristics). According to some examples, a usage rate ofan item may be calculated at 1104, whereby a sample may be a replica ofthe item (e.g., a consumable item). In some cases, a usage rate may bedetermined as a function of a depletion rate of the consumable item. Atime for a unit of consumption may be computed (e.g., predicted) basedon a calculated usage rate. Also, a unit of consumption may be selectedas a feedback commencement time during which to solicit feedback. In oneexample, an interval of time for a predicted unit of consumption may bedetermined similarly to a zone of time as described herein.

In some examples, a subset of the data (e.g., included in an electronicmessage) may be transmitted to generate feedback from a user computingdevice. Data representing a command to delay a feedback commencementtime may be received via, for instance, a merchant computing system.Further, transmission of another subset of the data (e.g., anotherelectronic message including feedback) may be postponed by an amount oftime. For example, a recipient may request a certain number ofadditional days to provide feedback. Or, a second electronic message todelay feedback may be transmitted based on predicted or empirical data.Further, a predicted date of consumption may be adapted to form anadapted predicted date of consumption. For example, data representing afeedback commencement time (e.g., a point in time or an interval inwhich feedback may be solicited) may be modified based on an adaptedpredicted distribution date to form a modified feedback commencementtime.

At 1108, data to cause shipment of a replica may be generated toinitiate shipment of the replica or sample to a location associated withthe electronic account. For example, an application or software modulemay include executable instructions to modify or control a shippingapplication (e.g., an enterprise-level application configured tocoordinate shipping of online purchases) to ship a purchased item in acontainer. In some cases, the selection of the sample may be based onthe above-described characteristic. At 1110, a subset of data disposedin an electronic message may be generated to cause initiation ofexecutable instructions to perform a process in which feedback may begenerated relative to a date of consumption.

FIG. 12 is a diagram depicting examples of integrated interfaces tofacilitate predictions of sample consumption, according to someembodiments. Diagram 1200 depicts merchant computing systems 1230 a,1230 b, and 1230 n, each of which may be configured to host or generate(via network 1220 a) user interfaces 1209 and 1220 that are integratedwith interface portions 1215 and 1224, respectively. Interface portions1215 and 1224 may generated by adaptive distribution platform 1210,which includes information feedback predictor 1214 a. Thus, informationfeedback predictor 1214 a may be configured to inject interface portions1215 and 1224 into user interfaces 1209 and 1220, respectively, vianetwork 1220 b to form hybrid interfaces. Thus, web pate 1209 and email1220 are hybrid electronic pages or messages and may include graphicalelements having a “look and feel” originating from a source of origin(e.g., a merchant computing system as host). According to some examples,elements depicted in diagram 1200 of FIG. 12 may include structuresand/or functions as similarly-named or similarly-numbered elementsdepicted in other drawings.

Various structures and/or methods described herein may be applied to webpages 1209, emails 1220, among other forms of communicating requests foracquiring feedback for a sample. Web page 1209 includes a feedback pageas a user interface 1212 to provide feedback of a sample (e.g., ashampoo sample) by selecting in interface portion 1215 either immediatefeedback via input 1211 (e.g., including an order) or delayed feedbackvia input 1213. Input 1219 (a pull-down menu to select an amount ofdelay) may be used to delay a feedback response by “t” units (e.g., anytime units, such as units of days). Data received into interface 1212via inputs 1211, 1213, and 1219 may be stored until input (“send”) 1218is activated, after which the data may be transmitted contemporaneously(or substantially contemporaneously) to information feedback predictor1214 a and adaptive distribution platform 1210.

An electronic message to request feedback of the sample may becommunicated as an email 1220, which is shown to include an itemcharacteristic 1222 in a subject line may be display so a user readilymay discern the action required for such a communication. In email body1224, various delivery options may be embedded as hypertext links toenable a user to provide feedback for a sample of a “Product P” “now,”by selecting link 1275 a, or postponing feedback by 7 days with theselection of link 1275 b. According to some embodiments, thepresentation of a link for a “7 day” delay, as a first link, may be dueto its relatively high degree of certainty that a user may experiencethe sample within another 7 days (based on probabilisticdeterminations). Thus, adaptive distribution platform 1210 includinginformation feedback predictor 1214 a may adapt presentation of userinputs to accommodate user purchasing and scheduling patterns andpreferences to enhance, among other things, users' experiences whensampling a good or service.

In view of the foregoing, webpages 1209 and emails 1220 may be formed asintegrated electronic messages that are a hybrid of different formatteddata originating from different sources, whereby a user may perceivewebpages 1210 and emails 1220 as originating from a single source (e.g.,a merchant).

Although the foregoing examples have been described in some detail forpurposes of clarity of understanding, the above-described inventivetechniques are not limited to the details provided. There are manyalternative ways of implementing the above-described inventiontechniques. The disclosed examples are illustrative and not restrictive.

1. A method comprising: identifying data representing a replica as asample of an item for transmission to a location associated with anelectronic account; identifying an item characteristic of the item;predicting at a computing device a date of consumption of the replica toform a predicted date of consumption as a function of the itemcharacteristic; generating data to cause shipment of the replica to thelocation associated with an electronic account; and generating a subsetof data to initiate executable instructions to generate feedbackregarding the replica relative to the date of consumption.
 2. The methodof claim 1 further comprising: receiving data representing a feedbackresponse to accept a unit of the item; computing of the item based on ausage rate as a consumable item; and causing scheduling of transmissionof the unit of the item to the location associated with electronicaccount, wherein the scheduling is a function of predicted date ofdistribution.
 3. The method of claim 1 further comprising: transmittingdata representing a feedback request as the subset of the data togenerate feedback; receiving adjustment data responsive to the data togenerate feedback, the adjustment data configured to modify thepredicted date of consumption; and causing generation of another subsetof data to generate ancillary data representative of a feedbackresponse.
 4. The method of claim 1 wherein predicting the date ofconsumption comprises: computing a time interval of transit to thelocation association with the electronic account; and determining apredicted point of depletion.
 5. The method of claim 4 wherein furthercomprising: computing a predicted time interval at which a feedbackresponse is available relative to the time interval of transit and thepredicted point of depletion.
 6. The method of claim 1 furthercomprising: forming a communication channel to exchange electronicmessages including a feedback response associated with the electronicaccount.
 7. The method of claim 6 wherein forming the communicationchannel comprises: transmitting the subset of data as an electronicmessage formatted as a short message service (“SMS”) message.
 8. Themethod of claim 1 wherein predicting the date of consumption of thereplica comprises: calculating a usage rate of the item as a consumableitem; predicting a time for a unit of consumption based on thecalculated usage rate; and selecting the unit of consumption as afeedback commencement time during which to solicit feedback.
 9. Themethod of claim 8 further comprising: transmitting the subset of thedata to generate feedback; receiving at a merchant computing system datarepresenting a command to delay the feedback commencement time;postponing transmission of another subset of the data to the merchantcomputing system an amount of time expires; and releasing thetransmission of the another subset of the data
 10. The method of claim 8further comprising: adapting a predicted date of consumption to form anadapted predicted date of consumption; and modifying data representingthe feedback commencement time based on the adapted predicteddistribution date to form a modified feedback commencement time.
 11. Themethod of claim 1 further comprising: selecting the replica as thesample.
 12. The method of claim 11 wherein selecting the replicacomprises: analyzing data representing user-related characteristicassociated with the electronic account against other user-relatedcharacteristics associated with other electronic accounts; identifying atargeted item associated with the other electronic accounts; andselecting the targeted item as the item.
 13. The method of claim 12further comprising: selecting a portion of the item as the replica. 14.The method of claim 1 further comprising: formatting data representing arequest for feedback to solicit to form formatted data to integrate withan electronic message generated for a merchant computing system; andtransmitting the formatted data to a user interface to display a displayportion based on the formatted data as an integrated portion of anintegrated electronic message including the electronic message.
 15. Themethod of claim 1 further comprising: formatting data representing arequest for feedback to solicit to form formatted data to integrate witha web page generated for a merchant computing system; and transmittingthe formatted data to a user interface to display a display portionbased on the formatted data as an integrated portion of an integratedweb page including the web page.
 16. An apparatus comprising: a memoryincluding executable instructions; and a processor, responsive toexecuting the instructions, is configured to: identify data representinga replica as a sample of an item for transmission to a locationassociated with an electronic account; identify an item characteristicof the item; predict at a computing device a date of consumption of thereplica to form a predicted date of consumption as a function of theitem characteristics; generate data to cause shipment of the replica tothe location associated with an electronic account; and generate asubset of data to initiate executable instructions to generate feedbackregarding the replica relative to the date of consumption.
 17. Theapparatus of claim 16, wherein the processor is further configured to:receive data representing a feedback response to accept a unit of theitem; compute of the item based on a usage rate as a consumable item;and cause scheduling of transmission of the unit of the item to thelocation associated with electronic account.
 18. The apparatus of claim16, wherein the processor is further configured to: transmit the subsetof the data to generate feedback; receive adjustment data responsive tothe data to generate feedback, the adjustment data configured to modifythe predicted date of consumption; and cause generation of anothersubset of data to generate ancillary data representative of a feedbackresponse.
 19. The apparatus of claim 16, wherein the processor isfurther configured to: compute a predicted time interval of transit tothe location association with the electronic account; and compute apredicted point in time at which a feedback response is available. 20.The apparatus of claim 16, wherein the processor is further configuredto: format data representing a request for feedback to solicit to formformatted data to integrate with an electronic message generated for amerchant computing system; and transmit the formatted data to a userinterface to display a display portion based on the formatted data as anintegrated portion of an integrated electronic message including theelectronic message.